Careers at ServiceNow

This role is for an engineer who is beginning their software engineering career and learning to build reliable, maintainable AI-native and cloud-native software. The IC2 engineer works on well-defined features and components with close guidance from senior engineers and tech leads. They focus on developing solid software engineering fundamentals, gaining hands-on experience with modern development practices, and learning to integrate AI/ML services into applications. This role provides mentorship and opportunities to grow technical depth in Python, Java, JavaScript, and ServiceNow platform development while contributing meaningfully to the product. Responsibility Area: Software Development • Develop software features and components under the direction of senior engineers; take ownership of well-defined, scoped tasks • Write clean, maintainable code with comprehensive unit tests; follow established coding standards and best practices • Contribute to the implementation of AI-native features, including prompt design assistance, API integration, and basic evaluation • Work with assigned features from design through development, testing, and deployment with guidance from teammates • Troubleshoot and debug issues in development and staging environments; escalate complex issues to senior engineers • Follow secure coding practices, including responsible handling of customer data and AI safety considerations • Participate in code reviews and actively incorporate feedback from senior engineers • Learn and apply CI/CD practices; contribute to automated testing and deployment pipelines Responsibility Area: ServiceNow Platform Development • Develop features and customizations on the ServiceNow platform under supervision • Write ServiceNow workflows, business rules, and automation using JavaScript and GlideScript with guidance • Build forms and dashboards on ServiceNow; learn data model design and query optimization techniques • Contribute to ServiceNow plugin and app development following established patterns and best practices • Learn ServiceNow APIs, web services, and integration patterns through hands-on implementation • Participate in ServiceNow application maintenance and support for assigned features • Seek to understand ServiceNow platform capabilities and how they apply to business problems Responsibility Area: Learning & Collaboration • Learn from code reviews, pair programming, and mentorship from senior engineers • Ask clarifying questions and seek guidance when encountering unfamiliar technical challenges • Actively participate in team discussions, design reviews, and sprint planning • Communicate clearly about progress, blockers, and technical challenges • Participate in knowledge-sharing sessions and team learning opportunities • Support junior teammates and newer developers by sharing what you've learned • Take initiative to understand the broader system and how your work fits into the product Responsibility Area: Customer Support & Quality • Participate in customer issue triage and bug fixing under supervision • Assist senior engineers in troubleshooting customer-reported issues and understanding root causes • Contribute to customer documentation and internal knowledge bases • Learn from customer issues and help identify patterns that could prevent future problems • Handle support tickets for issues in your area with guidance from teammates • Communicate respectfully and professionally with customers and internal teams • Seek to understand customer needs and how they use the application Bachelor's degree in computer science, engineering, or related field (or equivalent practical experience) 2-5 years of professional software development experience Proficiency in at least one programming language (Python, Java, or JavaScript); willingness to learn others Understanding of fundamental software engineering concepts: version control, testing, debugging Basic knowledge of relational databases and SQL Familiarity with RESTful APIs and modern web services Strong problem-solving skills and ability to learn quickly from feedback Good communication skills and ability to work collaboratively with a team Understanding of AI/ML concepts; hands-on experience with LLMs or AI APIs a plus Prior experience or coursework with ServiceNow platform is a plus Experience with Agile development methodologies is a plus Ability to take direction, ask for help when needed, and work independently on assigned tasks Passion for software quality, testing, and continuous learning Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

As a Staff Data Scientist at ServiceNow, you’ll play a highly embedded and influential role within our Data Science organisation, partnering closely with Development and Product teams across multiple products within ITOM. Rather than simply responding to predefined requirements, you’ll bring your own creativity, domain expertise, and ideas to identify opportunities, challenge assumptions, and solve complex problems using real customer data. What you get to do in this role: Lead the development and evolution of advanced data science and machine learning capabilities, including noise reduction, anomaly detection, root cause analysis, and new AI-powered product features. Own complex data science problems end-to-end, from problem definition and ideation through experimentation, model development, evaluation, productionization, and deployment. Develop advanced algorithms and models using techniques such as machine learning, deep learning, NLP, anomaly detection, and statistical modeling. Partner closely with Product, Engineering, Quality, and Architects to translate product requirements into scalable, reliable ML solutions. Drive technical direction and mentor other data scientists, contributing to best practices in modeling, experimentation, evaluation, and ML engineering. Stay current with advances in AI/ML and identify opportunities to apply emerging technologies to ServiceNow products. To be successful in this role you have: Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. 6+ years of experience in data science, machine learning, algorithm development, or a related field. Advanced expertise in at least one area such as anomaly detection, machine learning, deep learning/neural networks, NLP, or statistical modeling. Proven experience leading complex data science or machine learning problems from ideation to production. Strong Python skills and hands-on experience with modern data science and ML frameworks. Experience working with large and complex datasets, developing evaluation methodologies, and defining metrics for ML models and data-driven products. Practical experience with modern AI/ML techniques, including LLMs and/or generative AI. Strong technical leadership, problem-solving, communication, and cross-functional collaboration skills. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

Role summary The Staff Software Engineer (IC4) on HR Service Delivery designs, builds, ships, and operates capabilities whose core behavior is model-driven rather than explicitly authored: agentic and conversational experiences that interpret an employee's, manager's, or HR agent's intent, reason over employee profile, case, catalog, policy, and knowledge context, invoke tools, and act on the user's behalf across the employee lifecycle. This is not a machine learning or AI research role; the engineer does not train foundation models. It is also distinct from traditional full-stack engineering, where systems follow deterministic logic rather than selecting execution paths at runtime. Two consequences shape the work. First, the most important logic often lives in natural language: instructions, prompts, tool descriptions, guardrails, escalation rules, all of which must be engineered, versioned, and reviewed with the same discipline as code. Second, because behavior is probabilistic, correctness is established by measuring behavior at scale rather than by asserting fixed outputs, making automated evaluation a first-class engineering activity rather than a quality-assurance afterthought. HR sharpens both points along two independent axes. Accuracy. An agent answering on payroll, benefits, leave, or a lifecycle event touches statutory entitlement, jurisdiction-specific policy, and an employee's pay. A confidently wrong output is not a bad answer; it is a missed enrollment window or an incorrect leave balance acted on in good faith. Audience. HR data is among the most sensitive on the platform, and correctness of content is not sufficient. An answer grounded in a record or knowledge article the requestor is not entitled to read is a data exposure even when every fact in it is true, and manager-scope and employee-scope views of the same question have different correct answers. Access boundaries have to be enforced in the retrieval and tool layer rather than requested of the model. At IC4 the engineer owns AI design decisions across the domain, not within a single feature, and owns the correctness of what ships whether a person or an agent produced it. What you do Build AI-native capability across the employee lifecycle Design and ship features built around agentic behavior — intent interpretation, multi-step reasoning, tool invocation, and action on the user's behalf — together with the data models, integrations, and channels that make them usable in production. In HRSD this spans guided service selection and intake, natural-language case creation and enrichment, conversational case status and in-flight change, tiered resolution of payroll and benefits inquiries, case triage, routing, and deflection, eligibility and entitlement inquiry, and agent-driven execution of onboarding, transfer, and offboarding lifecycle events. Design AI-driven autonomous workflows Decompose HR processes into the steps and decision points an agent can execute: determining where autonomy is appropriate, where a checkpoint with a person is required, and how exceptions, retries, and hand-back are handled. Anything that changes pay, employment status, or a restricted record, and anything touching employee relations or investigation, needs a human decision point by construction. The design must make that distinction structural rather than advisory. Author and maintain agentic instructions as engineering artifacts Write, structure, and version the system instructions, role definitions, tool descriptions, guardrails, and escalation paths that govern agent behavior in the domain, under code review, source control, and regression coverage. Own the shared instruction and tool-description surface that adjacent teams build against. Because HRSD ships as product, customers configure, extend, and override that surface on their own instances: treat it as public API, with upgrade-safe extension points and versioning discipline to match. Build automated evaluation and test non-deterministic behavior Design and operate the evaluation that makes change safe: golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, and drift detection, plus adversarial, jailbreak, grounding, and tool-selection testing. Extend that coverage to the HR-specific failure classes — access-boundary violations in retrieval and citation, PII leakage across scopes, and jurisdictional and policy-variant correctness — and keep evaluation meaningful across customer configurations rather than against a single reference dataset. Own the resolution, containment, and quality metrics the domain is measured on, including whether they are instrumented correctly in the first place. Design conversational experiences across channels Build experiences that hold context across turns, hand off cleanly between automated and live HR agents, and behave consistently across employee-facing portals, chat shells, workplace messaging clients, agent workspace, and voice — accounting for what voice imposes: latency budgets, barge-in, speech recognition error on names and plan terminology, disambiguation, and explicit confirmation before consequential actions. Specify precisely and direct AI coding agents Convert requirements into testable specifications with explicit scope, constraints, non-goals, and acceptance criteria; decompose work into agent-sized tasks; supervise several workstreams in parallel; and review agent output for correctness, spec adherence, security, and maintainability. You own the result regardless of what produced it. Own quality, safety, and reliability in production Monitor conversation quality, containment, hallucination rate, tool-selection error, and unsafe or unauthorized action. Defend against prompt injection and data leakage across integration surfaces, including the paths where user-supplied content — case notes, inbound email, attachments, authored knowledge — enters agent context. Maintain reasoning-trace observability and model rollback mechanisms, and feed production failures back into specifications and evaluation sets. Because HR conversation content is itself restricted, design that observability to be diagnosable without exposing what was said. Lead root-cause analysis when agentic behavior deviates from intent, and hold the line between a genuine model failure and a platform or configuration failure presenting as one. Ground it in solid full-stack delivery Build the application, APIs, data models, and integrations around these capabilities: front-end experiences for employees, managers, and HR agents, server-side logic, and the connections to HCM, payroll, benefits, identity, knowledge, and the adjacent service domains HR cases cross into — with the CI/CD, observability, and upgrade-safe extensibility expected of production software. Collaborate across product, design, and engineering Partner with product managers, designers, conversation designers, HR domain and compliance partners, and engineers to define success criteria and communicate capability and risk clearly. Mentor IC1 to IC3 engineers, and raise the team's practices around instruction authoring, evaluation, and accountable agent use. Required experience and skills Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 9+ years software engineering experience (backend, frontend, or full stack) Strong JavaScript/Node.js, React and API integration skills Automated testing (unit + integration) and CI/CD pipeline experience Solid understanding of data modelling and query optimization Agentic delivery experience: Hands-on experience with AI/GenAI or ML-driven features (LLM prompting, NLU, classification models, or similar) Hands-on experience with LLM-integrated features: prompt design, context injection, output tuning, guardrails. Experience with NLU/intent classification or conversational AI systems Familiarity with ML-driven automation (classification, clustering, recommendation systems) Production AI integration. Experience integrating large language model APIs and retrieval-grounded features, including agent orchestration, tool and function calling, and structured output enforcement. Applied machine learning literacy. A working command of the concepts that govern how these systems behave — evaluation, embeddings, and the probabilistic output and failure modes of modern models — sufficient to reason about, debug, and verify model-driven behaviour in production. Accountable use of AI coding agents. Current, effective use of AI coding assistants and agents with evidence of accountable delivery: precise specification, critical review of generated output, and verification harnesses. Operational experience. Hands-on CI/CD, containerized workloads, and observability experience, plus direct on-call and incident-command experience with customer-facing systems. Mentorship. Demonstrated mentorship of less-experienced engineers and a record of raising quality through code review. Education. Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Advanced degrees are a plus but not a substitute for a record of shipping reliable AI-native applications. Preferred experience HR domain depth. HR case management, the employee lifecycle, or payroll, benefits, leave, and absence, at a depth sufficient to challenge a requirement rather than only implement it — including why the same policy question resolves differently by jurisdiction, employment type, or plan year. HCM and downstream integration. Integration with core HR, payroll, and benefits systems of record, including the reconciliation and eventual-consistency problems that come with treating an external system as the authority on employment data. Extensible product engineering. Building capability that customers configure, extend, and override on their own instances, where instruction and tool surfaces are versioned contracts rather than internal implementation. Evaluation and observability tooling. Evaluation frameworks, prompt and instruction management tooling, tracing for model-driven applications, and analysis of production transcripts at scale, particularly where transcript content is itself access-restricted. Conversational channel breadth. Employee self-service portals, virtual agent or chat shells, workplace messaging clients, and voice, including handoff between automated and live agents. Conversation design partnership. Working alongside conversation or content designers on dialogue flow, tone, and error-recovery design — with attention to how an HR answer is worded when the subject is the employee's own pay, health coverage, or employment status. Forward deployed delivery. Building against a customer's data, integrations, and channels, and tuning instructions and evaluation sets in their environment. Now Platform depth. Scoped applications, ACLs and platform security rules, Flow Designer, UI Builder, Automated Test Framework, and upgrade-safe extension patterns. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

Role summary The Senior Staff Software Engineer (IC5) on HR Service Delivery sets and owns the technical direction for model-driven capability across the HR domain and the surfaces adjacent domains build on top of it. The work is agentic and conversational experiences that interpret an employee's, manager's, or HR agent's intent, reason over profile, case, catalog, policy, and knowledge context, invoke tools, and act on the user's behalf — but the unit of ownership is the architecture, the standards, and the evaluation infrastructure that let multiple teams ship those experiences safely, not a set of features. This is not a machine learning or AI research role; the engineer does not train foundation models. It is also distinct from traditional staff-level full-stack engineering, where systems follow deterministic logic rather than selecting execution paths at runtime. Two consequences shape the work. First, the most important logic often lives in natural language — instructions, prompts, tool descriptions, guardrails, escalation rules — which must be engineered, versioned, and reviewed with the same discipline as code, and which needs an architecture once more than one team is authoring it. Second, because behavior is probabilistic, correctness is established by measuring behavior at scale rather than by asserting fixed outputs, which makes evaluation a platform investment rather than a per-feature activity. HR sharpens both points along two independent axes. Accuracy. An agent answering on payroll, benefits, leave, or a lifecycle event touches statutory entitlement, jurisdiction-specific policy, and an employee's pay. A confidently wrong output is not a bad answer; it is a missed enrollment window or an incorrect leave balance acted on in good faith. Audience. HR data is among the most sensitive on the platform, and correctness of content is not sufficient. An answer grounded in a record or knowledge article the requestor is not entitled to read is a data exposure even when every fact in it is true, and manager-scope and employee-scope views of the same question have different correct answers. Enforcement belongs in the retrieval and tool layer, not in a request to the model, and at this level you own that being true by construction across every team building in the domain. The distinguishing expectation at IC5 is that the hardest problems arrive unframed. You decide what the domain should do about them, commit the organization to an approach, and are accountable for that approach across releases — including for the decisions that turn out wrong. What you do Set the AI architecture for the domain Own the architecture of model-driven capability across the employee lifecycle: how agents are decomposed and composed, where reasoning happens, how context is assembled and bounded, how tools are exposed and described, and how autonomy is delegated and revoked. Establish the reference patterns other teams build against, and the boundaries between what is a shared platform concern and what each product area owns. Make the calls with multi-release consequences — model selection and migration, orchestration approach, build-versus-adopt, and the cost, latency, and quality tradeoffs behind each — and own the outcome. Define where autonomy goes, and where it does not Decide, as a matter of domain policy rather than per-feature design, which HR actions an agent may take, which require a human decision point, and which no agent should attempt. Anything that changes pay, employment status, or a restricted record, and anything touching employee relations or investigation, needs a human in the path by construction. Then build the mechanisms that make those constraints structural and hard to violate accidentally, so that a team shipping a new capability inherits the boundary instead of re-deriving it. You hold the authority to refuse a shipping decision on these grounds, and are expected to use it. Own the shared instruction and tool-description architecture HRSD ships as product: customers configure, extend, and override the instruction and tool surface on their own instances, and adjacent domains build against it. Treat it as a versioned contract with upgrade-safe extension points, deprecation paths, and compatibility guarantees. Own how that surface is structured, reviewed, and evolved across teams — including the authoring standards, the review bar, and the regression coverage that make natural-language logic maintainable at organizational scale rather than only within one codebase. Build the evaluation infrastructure the organization ships against Own evaluation as leverage: the golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection that let many teams change behavior safely and quickly. Extend coverage to the HR-specific failure classes — access-boundary violations in retrieval and citation, cross-scope leakage, jurisdictional and policy-variant correctness — and keep it meaningful across the range of customer configurations rather than a single reference environment. Define the resolution, containment, and quality metrics the domain is measured on, be the person who can say whether they are instrumented correctly, and raise the standard of evidence required before a behavioral change ships. Frame problems the organization has not yet framed Identify the risks, gaps, and structural weaknesses in model-driven behavior that no one has articulated yet, quantify them, and drive them to a decision. This includes the failure classes that only appear at scale or in specific customer environments, the second-order consequences of a model or platform change, and the places where current practice will not survive the next generation of capability. Bring these to product and engineering leadership with the analysis and the recommendation, not the problem alone. Direct AI coding agents, and set how the organization does Convert ambiguous problem statements into testable specifications with explicit scope, constraints, non-goals, and acceptance criteria; decompose work into agent-sized tasks; and supervise parallel workstreams. Beyond your own delivery, define what accountable agent-assisted engineering looks like for the domain — specification standards, review expectations, verification harnesses — and hold the line on it. You own the result regardless of what produced it, and you own the norm. Own production quality, safety, and reliability across the domain Own the observability and safety posture for agentic behavior: conversation quality, containment, hallucination rate, tool-selection error, unsafe or unauthorized action, and the paths where user-supplied content — case notes, inbound email, attachments, authored knowledge — enters agent context as an injection vector. Because HR conversation content is itself restricted, design diagnosis that works without exposing what was said. Lead root-cause analysis on the incidents no one else can resolve, hold the distinction between a genuine model failure and a platform or configuration failure presenting as one, and close the loop from production failure back into specification and evaluation. Deliver hands-on where it matters Stay in the code on the load-bearing parts: the hard integration, the risky migration, the prototype that settles an architectural argument, the incident nobody else can unblock. Delivery at this level is selective and deliberate rather than continuous, and the expectation is that your hands-on work resolves uncertainty for others rather than absorbing feature scope. Influence beyond the team Represent the domain's technical position to engineering and product leadership, to customers, and to partner organizations. Communicate capability and risk to non-engineering audiences without flattening either. Build alignment across teams that do not report to you and whose priorities compete. Grow staff-level engineers, raise the bar in technical review and hiring, and make the domain's practices around instruction authoring, evaluation, and accountable agent use durable enough to outlast your involvement in any one project. Required experience and skills Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 12+ years software engineering experience (backend, frontend, or full stack) Strong JavaScript/Node.js, React and API integration skills Automated testing (unit + integration) and CI/CD pipeline experience Solid understanding of data modelling and query optimization Agentic delivery experience: Hands-on experience with AI/GenAI or ML-driven features (LLM prompting, NLU, classification models, or similar) Hands-on experience with LLM-integrated features: prompt design, context injection, output tuning, guardrails. Experience with NLU/intent classification or conversational AI systems Familiarity with ML-driven automation (classification, clustering, recommendation systems) Production AI integration. Experience integrating large language model APIs and retrieval-grounded features, including agent orchestration, tool and function calling, and structured output enforcement. Applied machine learning literacy. A working command of the concepts that govern how these systems behave — evaluation, embeddings, and the probabilistic output and failure modes of modern models — sufficient to reason about, debug, and verify model-driven behaviour in production. Accountable use of AI coding agents. Current, effective use of AI coding assistants and agents with evidence of accountable delivery: precise specification, critical review of generated output, and verification harnesses. Operational experience. Hands-on CI/CD, containerized workloads, and observability experience, plus direct on-call and incident-command experience with customer-facing systems. Mentorship. Demonstrated mentorship of less-experienced engineers and a record of raising quality through code review. Education. Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience. Advanced degrees are a plus but not a substitute for a record of shipping reliable AI-native applications. Preferred experience HR domain depth. HR case management, the employee lifecycle, or payroll, benefits, leave, and absence, at a depth sufficient to challenge a requirement rather than only implement it — including why the same policy question resolves differently by jurisdiction, employment type, or plan year. HCM and downstream integration. Integration with core HR, payroll, and benefits systems of record, including the reconciliation and eventual-consistency problems that come with treating an external system as the authority on employment data. Extensible product engineering. Building capability that customers configure, extend, and override on their own instances, where instruction and tool surfaces are versioned contracts rather than internal implementation. Evaluation and observability tooling. Evaluation frameworks, prompt and instruction management tooling, tracing for model-driven applications, and analysis of production transcripts at scale, particularly where transcript content is itself access-restricted. Conversational channel breadth. Employee self-service portals, virtual agent or chat shells, workplace messaging clients, and voice, including handoff between automated and live agents. Conversation design partnership. Working alongside conversation or content designers on dialogue flow, tone, and error-recovery design — with attention to how an HR answer is worded when the subject is the employee's own pay, health coverage, or employment status. Forward deployed delivery. Building against a customer's data, integrations, and channels, and tuning instructions and evaluation sets in their environment. Now Platform depth. Scoped applications, ACLs and platform security rules, Flow Designer, UI Builder, Automated Test Framework, and upgrade-safe extension patterns. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

What you’ll do: Technical Architecture & Strategy Define and own the long-term technical architecture , making critical build vs. buy decisions to ensure systems remain resilient as the platform scales 10x Platform & Infrastructure Design and build core infrastructure services and microservices that serve machine learning, frontend, and platform teams Business Alignment Align technical architecture with business goals, ensuring engineering velocity , system reliability , and infrastructure costs directly support customer acquisition, retention, and time-to-market Hands-on Engineering Write high-performance , clean, and maintainable production-grade code across the stack (K8s, Python, Golang, Postgres etc.) Cross-functional Execution Drive and deliver business-critical engineering outcomes in close collaboration with cross-functional engineering teams Engineering Culture Mentor senior engineers, champion engineering excellence , and foster a culture of rapid, impact-driven innovation Ownership & Initiative Identify critical system and business gaps proactively, propose solutions, align stakeholders, and drive execution with a high degree of ownership Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry. Experience 15+ years of software engineering experience with a demonstrated progression into Principal or Sr. Staff Engineer capacity at a high-growth tech company or top-tier AI lab Technical Proficiency Strong hands-on coding ability with deep design thinking , capable of driving both code-level execution and system-level architecture decisions . Proficiency in any programming language with ability to adapt — K8s, Python, Golang, Postgres and data lakes preferred Distributed Systems Proven experience in distributed systems — performance, scalability, latency, optimization , and monitoring Commercial Mindset Demonstrated ability to connect engineering decisions to business outcomes such as latency improvements , driving margin efficiency , and enabling rapid product feature launches High Agency Self-driven with strong ownership , ability to identify system and business gaps, propose solutions, align stakeholders, and execute at startup pace Product & AI Mindset Experience integrating or evaluating AI tools and workflows in engineering processes and decision-making Good to Have Open Source Contributions to major open-source AI or infrastructure projects Startup Experience Prior experience working closely with Product and Growth teams in an early-stage startup environment Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

The Role Are you up for an exciting challenge? Picture yourself scaling and optimizing a cutting-edge Generative AI product that offers instant assistance to enterprise users. Ever wondered how to apply abstraction, scalability, and optimization to a dynamic, probabilistic, and generative conversational system? If so, we invite you to join our Conversation Engine team. At our company, you'll have the unique opportunity to work at the core of Moveworks Generative AI product. Your main focus will be scaling and optimizing it to meet the growing demands of the enterprise solution space. Take a look at our recent posts ( Moveworks Live , Creator Studio ) on Moveworks’s groundbreaking solutions for enterprise AI. As pioneers in enterprise AI, we don't follow a set formula for building the next solution. Instead, we encourage you to bring your innovative ideas and imagination to solve unsolved problems. You'll collaborate closely with machine learning experts and cross-functional teams, rapidly iterating over new ideas, and leveraging user behavior data to make informed decisions. Your challenge will be to scale and optimize our conversation engine to support hundreds of millions of users, across multiple chat channels and use cases, and enhance our end-to-end product infrastructure with the utmost engineering quality and robustness. What you get to do in this role: Construct an extensive product infrastructure, complete with user-friendly interfaces that empower engineers and configurators to effortlessly customize and optimize generative AI models. This includes collecting data and feedback, adapting to diverse enterprise use cases and business contexts, and delivering domain-specific conversational experiences. Design scalable API abstractions for our conversation platform, which supports all popular chat clients (for example, MSTeams, Slack, and Web) and offers a neutral API for developing other parts of the engineering system. Optimize the dialog engine to accommodate a wide range of conversational features, leveraging private domain knowledge unique to each enterprise customer in the cloud, and enable real-time multilingual translation. You will achieve this with minimal memory footprint, low latency, and streamlined development process for application engineers. Champion the best practices for coding patterns, API design, scalability, robustness, and optimization. Foster a culture of excellence and continuous improvements among fellow engineers. Provide comprehensive insights and visibility into the performance of our conversational AI product. This involves implementing a robust logging and tracing framework, user-friendly debugging and triaging tools, and automated metrics for efficient monitoring and analysis. Collaborate closely with ML engineers, application engineers, product teams, and customer support teams to drive the development of new features and scalability initiatives. To be successful in this role you have: A strong foundation in computer science and software engineering, coupled with expertise in building scalable systems. A deep understanding of clean, modular, and scalable API design. You have the ability to champion best coding practices and influence fellow engineers to uphold high standards of code quality and craftsmanship. A passion for optimizing systems and improving performance. You are well-versed in tracing, logging, and metrics frameworks, and possess a systematic approach to quickly identify and resolve latency bottlenecks, race conditions, and throughput limitations. The ability to independently research new requirements and develop innovative solutions. You thrive in an environment of fast-paced coding and execution, embracing rapid iterations to deliver results. Strong communication skills to effectively articulate rationales and design approaches. You have a cross-functional awareness that enables you to collaborate seamlessly with various teams. A bachelor's degree or higher in computer science or a related field, demonstrating your academic foundation in the field. 7+ years of professional development experience, specifically in building systems at scale. For positions in this location, we offer a base pay of $176,100-308,200, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

Team: Our Site Reliability Engineering (SRE) team consists of highly skilled engineers responsible for maintaining and enhancing the reliability, scalability, and performance of the ServiceNow infrastructure. Our SRE’s are empowered to resolve technical issues across the entire technology stack, from hardware to applications. Additionally, they work to improve the platform's operability, aiming to reduce the number of incidents and minimize Mean Time to Recovery (MTTR). To achieve this, the team combines software development, networking, database, and systems engineering skills to tackle complex problems, striving to maintain our platform operating for our customers. Role: We are looking for a Director of Site Reliability Engineering to lead the next phase of our reliability transformation as ServiceNow modernizes toward a cloud-agnostic, cloud-ready production platform. This leader will own key elements of the SRE operating model across Reliability Engineering, Service Enablement, Service Registry, SLI/SLO standards, reliability governance, automation, AI-enabled operations, and production readiness . The role will lead a global engineering organization and partner across Product Engineering, Infrastructure, Architecture, Security, Release Engineering, and Customer Support to establish consistent reliability practices across ServiceNow products and services. The Director will play a critical role in evolving the organization from reactive operations toward an engineering-led SRE model focused on prevention, automation, resilience, and continuous improvement . What you get to do in this role: Define and execute the SRE strategy and operating model across reliability engineering, service enablement, observability, automation, incident learning, and production readiness. Lead and develop a global organization of engineering managers, technical leaders, and SREs. Establish enterprise reliability standards for service ownership, tiering, golden signals, SLIs/SLOs, error budgets, alerting, on-call practices, and service health reviews. Lead the Service Enablement strategy by establishing minimum reliability requirements and maturity standards for critical services. Own the Service Registry strategy, improving service ownership, dependency visibility, maturity tracking, and impact-aware operational decision-making. Drive adoption of SLIs, SLOs, error budgets, and burn-rate alerting across critical services, ensuring teams consistently use reliability signals to manage customer impact. Build a culture of engineering away toil by turning recurring operational work and incident patterns into automation, self-service, and systemic fixes. Establish the AI-enabled SRE roadmap, including change-risk assessment, operational insights, remediation recommendations, and policy-driven automation. Drive reliability and production-readiness strategy across AWS, Azure, and GCP by establishing cloud-agnostic patterns while addressing hyperscaler-specific operational requirements. Partner with product and platform engineers to design, launch, and operate reliable services throughout the production lifecycle. Establish launch and production-readiness practices that validate availability, latency, performance, capacity, dependencies, rollback, and recovery before customer impact. Drive sustainable operations by scaling self-service capabilities, automation platforms, and systemic reliability improvements across engineering teams. Lead incident response, blameless postmortems, and corrective actions that convert production failures into lasting reliability improvements. Measure reliability through SLIs, SLOs, error budgets, golden signals, change failure rate, MTTR, capacity health, and toil reduction. Influence architecture and platform direction to simplify operating models and improve reliability across ServiceNow's global infrastructure. Partner with executive and engineering leaders to prioritize reliability investments and drive adoption beyond the direct SRE organization. To be successful in this role you have: Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 12 years of significant leadership experience in Site Reliability Engineering, Production Engineering, Platform Engineering, Cloud Infrastructure, or large-scale distributed systems with a Bachelor's degree; or 8 years and a Master's degree; or a PhD with 5 years experience; or equivalent experience. Proven success leading managers and senior technical leaders across geographically distributed engineering organizations. Demonstrated success leading SRE, infrastructure, or reliability transformation at scale. Strong understanding of SLIs/SLOs, error budgets, observability, incident management, reliability governance, and on-call practices. Experience with service catalogs, service registries, service ownership models, Backstage, CMDB, dependency mapping, or service topology. Strong background in cloud infrastructure and modernization across AWS, Azure, and/or GCP. Understanding of Kubernetes, distributed systems, networking, databases, infrastructure automation, and cloud-native architecture. Experience driving automation through orchestration, Infrastructure as Code, self-service platforms, and auto-remediation. Familiarity with AI-assisted operations, autonomous remediation, or agentic technologies is highly desirable. Experience establishing production-readiness practices for releases, resilience, disaster recovery, infrastructure changes, and cloud migrations. Ability to use incident, reliability, and operational data to prioritize engineering work and drive systemic improvements. Strong cross-functional influence and executive communication skills. Ability to operate effectively through ambiguity, organizational transformation, and large-scale technical change. For positions in this location, we offer a base pay of $221,200 - $387,100 , plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

We are looking for a Senior Software Engineer with strong full-stack expertise — someone who can build rich, responsive frontends, design and scale robust backend services, develop and operate distributed systems in production. You will work across the entire stack, from customer-facing experiences to the high-throughput services and pipelines that power them, and increasingly build AI-driven capabilities and agents into the products you own — from design through production operation. What you get to do in this role: Design, build, and operate full-stack capabilities spanning backend services,frontend experiences and data pipelines. Build and scale backend services in Java and Python, designing clean, well-documented REST APIs with a focus on reliability and performance. Deploy, scale, and troubleshoot services on Kubernetes. Develop and operate distributed, high-throughput systems for ingestion, streaming, and storage, including Kafka-based streaming pipelines and ClickHouse-backed data stores. Develop performant, reusable frontend components using ReactJS or Angular with modern JavaScript/TypeScript. Design and integrate AI-powered features and agentic workflows into products — applying LLMs, retrieval, and agent frameworks to solve real customer problems. Partner with product management, design, and peer engineering teams to take features from concept to production. Own quality end to end — instrumentation, observability, testing, and performance at scale. Participate in design and code reviews and help raise the technical bar across the team. To be successful in this role you have: Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 6+ years of professional software engineering experience building and operating production systems. Backend proficiency in both Java and Python, with hands-on experience designing and building REST APIs. Hands-on experience with Kubernetes and containerization (Docker), including deploying and operating services in production. Desired frontend skills: ReactJS or Angular, JavaScript/TypeScript, and solid HTML/CSS fundamentals. Demonstrated experience designing distributed systems — scalability, fault tolerance, and messaging/streaming (e.g., Kafka). Hands-on experience or strong working knowledge of AI/ML — building with LLMs, AI agents, MCP or agentic frameworks, and applying them in production or product contexts. Working knowledge of data stores and pipelines, including streaming systems such as Kafka; experience with analytical databases such as ClickHouse is a plus. Familiarity with CI/CD and cloud-native development practices. A degree in Computer Science or a related field, or equivalent practical experience. Strong communication and collaboration skills, with a bias toward ownership and action. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

Build out core infrastructure services and microservices that impact our machine learning, frontend, and platform teams. Build out core infrastructure for various functionalities such as: distributed configuration management, rate limiting, feature flag, A/B testing, and traffic capture and replay. Improve the performance, scalability and observability of the Moveworks cloud infrastructure. Deliver deadline sensitive work regularly that is interdependent with other engineering teams. Own features end-to-end, and regularly influence the infrastructure roadmap. Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 12+ years of experience designing, building, shipping, and maintaining backend distributed systems. Familiarity with Java/Python/Golang/C++. Experience with containers/Docker, and with cloud infrastructure like AWS/GCP/Azure. Desire to work at a startup pace with a high degree of ownership. Experience solving for performance, optimization, scalability, latency, and monitoring. Strong motivation, gumption, and an appetite for continuous, incremental changes and completing challenging projects quickly. BS in computer science or a related field. High level of curiosity about engineering outside of your immediate discipline and an incessant desire to learn. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

This is a role for a hardened technical engineer who thrives on complexity and demands architectural excellence. You won't just build connectors; you will design the foundation of our integration layer. You will bridge the gap between legacy security stacks and cutting-edge AI-native agents, creating a unified framework where data interoperability meets autonomous reasoning. You will be a key driver in transforming our integration factory into a self-service, high-performance platform capable of orchestrating security at a global scale. Architect & Lead: Design and evolve highly resilient, distributed integration architectures (APIs, webhooks, streaming data pipelines) that can handle enterprise-scale throughput with sub-par latency. Pioneer AI-Native Systems: Lead the shift from ingestion-only integrations to autonomous security orchestration, integrating LLM-based reasoning and agentic workflows into our core product stack. Technical Excellence: Build scalable libraries and unified data models that allow our platform to "speak the language" of the global security ecosystem. Drive Vision: Partner directly with Product and R&D leadership to prototype and ship high-impact features that solve critical, real-world security challenges. Mentorship & Strategy: Set the technical standard for the engineering organization, mentoring senior engineers and driving code quality, system performance, and innovation across the team. Experience: 15+ years of deep, hands-on software engineering experience, with a proven track record of designing production-grade, distributed systems. Engineering Mastery: Expert-level proficiency in Python, with a deep understanding of memory management, concurrency models, and performance optimization. Deep Networking: Mastery of networking fundamentals (TCP/IP, L3-L7 protocols) and security infrastructure. System Design: Expert at building highly available, fault-tolerant, and horizontally scalable distributed architectures (Kafka, message queues, microservices). AI/ML Integration: Demonstrated experience or a deep desire to master LLM-powered application development (RAG, agentic workflows, loop engineering). Product-First Mindset: You treat system design as a business problem. You possess the ability to articulate complex technical trade-offs to stakeholders while remaining focused on delivering outsized value to our customers. Education/Background: Preferably MS or Phd in Computer Science or equivalent experience in a highly technical, fast-paced environment. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.