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What you get to do in this role We are looking for a Senior Manager of Machine Learning Engineering to lead a team building the next generation of AI Search and Retrieval capabilities that power intelligent experiences across the ServiceNow platform including Conversational and Agentic experiences. You will lead engineers and machine learning practitioners responsible for designing and delivering highly scalable search, retrieval, ranking, recommendation, and RAG (Retrieval-Augmented Generation) systems that enable users to discover information, generate insights, and accomplish work more effectively. This leader will collaborate closely with Product Management, Applied AI, Platform Engineering, UX, and Quality Engineering to define strategy, execute roadmap commitments, and deliver AI-powered experiences that serve millions of users globally. You will play a critical role in advancing the technologies powering ServiceNow AI Search, Knowledge Retrieval, and GenAI experiences, while building a high-performing engineering culture focused on innovation, quality, and business impact. Responsibilities Lead and grow a team of software engineers and machine learning engineers building large-scale AI Search and retrieval systems. Drive the strategy, architecture, and execution for Search, Ranking, Relevance, Personalization, and RAG capabilities. Partner with Product Managers and architects to define and deliver innovative AI-powered experiences. Build scalable retrieval pipelines leveraging vector search, semantic search, ranking models, and large language models. Improve relevance, precision, recall, and overall search quality across structured and unstructured enterprise content. Establish architectural patterns for highly available, resilient, and performant distributed systems. Lead the development and production deployment of machine learning models and retrieval services at enterprise scale. Drive operational excellence, reliability, observability, and performance optimization across the platform. Mentor engineering leaders and individual contributors while fostering a culture of collaboration, innovation, and technical excellence. Partner with cross-functional stakeholders to evaluate emerging AI technologies and accelerate their adoption into customer-facing products. Balance short-term delivery goals with long-term technical investments and platform evolution. To be successful in this role you have: 12+ years of software engineering experience with a Bachelor's degree, or 10+ years with a Master's degree, or equivalent experience. 3+ years of engineering management experience leading high-performing technical teams. Strong expertise in Java or Python and large-scale distributed systems. Hands-on experience with Search technologies such as Lucene, Elasticsearch, Solr, OpenSearch, Algolia, Coveo, Glean, or similar platforms. Experience building and operating large-scale retrieval, ranking, recommendation, or search systems. Experience with modern AI/ML technologies, including LLMs, embeddings, vector databases, and RAG architectures. Strong understanding of information retrieval, search relevance, ranking algorithms, and semantic search. Experience deploying and operating machine learning models in production environments. Knowledge of cloud-native architectures, microservices, observability, and performance optimization. Strong collaboration and communication skills with the ability to influence across engineering and product organizations. Proven track record of delivering innovative products from concept through production at enterprise scale. Preferred Qualifications Experience building enterprise search, knowledge retrieval, or GenAI products. Experience with personalization, recommendation systems, or relevance optimization. Experience working with vector search technologies and embedding frameworks. Experience leading geographically distributed engineering teams. Experience integrating AI capabilities into customer-facing SaaS products. Why this role is exciting Own a critical part of ServiceNow's AI platform strategy. Influence how millions of users discover knowledge and interact with AI-powered experiences. Lead a team at the intersection of Search, Machine Learning, and Agentic platform. Partner with world-class engineers, researchers, and product leaders. Help shape the future of AI-driven enterprise productivity. For positions in this location, we offer a base pay of $190,900 - $334,100 , 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.

Position Overview: ServiceNow is seeking a highly motivated and experienced Staff Software Engineer in Platform Persistence to solve complex problems in management of data lifecycles within the ServiceNow ecosystem. This pivotal role focuses on ensuring the efficient stewardship of data within our platform. The Platform Persistence team is at the forefront of some of the most impactful projects, offering visibility to the executive leadership. As part of this team, you will play a crucial role in shaping the future of our platform’s data architecture and ensuring that data is stored, retrieved, and managed efficiently. In addition to traditional data management, this team will have a unique focus on telemetry and data visualization, making a substantial impact on how customer and platform data is utilized and presented. What you get to do in this role: Writing highly scalable code leveraging best practices on concurrency and memory utilization. Contribute to data management strategies, ensuring scalability, efficiency, and security. Collaborate with cross-functional teams to implement data-related initiatives. Explore and experiment with challenging data problems including but not limited to data loss detection & recovery. Champion data best practices and security standards across the organization. Monitor and optimize data performance, troubleshooting and implementing improvements as needed. Stay current with the latest data storage and management technologies, including distributed storage systems (S3, Apache Iceberg) and Kubernetes-based deployments. Use AI tools to help solve challenging data and distributed systems problems at scale, applying sound judgment on guardrails and process for AI-led software development. Fosters a culture of innovation and continuous improvement. To Be Successful in this role you have: 8+ years of software development experience with a Bachelor's degree; OR 5+ years with Master's degree; OR 3+ years with a PhD; OR equivalent work experience. Proficiency in Java programming and strong expertise in data structures and algorithms. Proficiency with concurrency and parallel programming concepts. Working knowledge of databases, data modeling, and data storage technologies. Strong relational database experience to handle large amount of data on relational systems. Deep knowledge of Kubernetes and hands-on experience with Kubernetes service deployment is a strong plus. Working knowledge of distributed storage systems such as S3 and Apache Iceberg. Strong understanding of cloud-based and distributed systems. Familiarity with ServiceNow platform is a plus. Excellent problem-solving and communication skills. Working knowledge of JavaScript is beneficial for cross-functional collaboration. Experience with AI-assisted development tools and awareness of related guardrails/process is a plus. 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, 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 the Platform Engineering Application Life Cycle Team and we are looking for a backend engineer to help join a dynamic environment. We are responsible for the life cycle of an application and so this individual will support us by building/upgrading applications as well as on the platform. Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.) Partner with developers and product owners to understand requirements and own your code from design, implementation, test automation and delivery of high-quality product to our users. Help design software that is simple to use to allow customers to extend and customize the functionality to meet their specific needs. Help design and implement new products and features while also enhancing the existing product suite To be successful in this role: Passionate about learning and applying AI to solve Developer needs Passion for writing code in Java and leveraging the web as a platform, with a strong focus on reusability and componentization Experience with data structures, algorithms, object-oriented design, design patterns, and performance/scale considerations Working knowledge and ability to use tools to assist with daily tasks (IDE, debugger, build tools, source control, ServiceNow instances, profilers, system administration/Unix tools) 2-5 years proficiency in Java, Javascript; AWS background is a plus 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 In using AI Productivity tools such as Windsurf, Cursor, etc is a plus or nice to have. For positions in this location, we offer a base pay of $125,700 - $194,800 , 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.

**This role requires in-office presence in Mountain View, CA 5x/week** About the Team The Developer Experience team builds platforms that enable developers building on the Moveworks Agentic AI platform. We own the flywheel: more builders → more integrations shipped → better assistant → more demand. We build scalable infrastructure first, then the products on top of it. Platform engineering discipline meets developer-facing product thinking. The Role You own the technical foundation the team ships on. You design and build the hardest platform problems — generality, scalability, extensibility — while keeping developer experience as the measure. You think in systems. You ship. You bring others along. What You'll Do Design and build core developer platforms: SDKs, APIs, runtime abstractions, tooling infrastructure, developer-facing services Own end-to-end delivery of products that increase developer productivity: faster time-to-market, higher-quality artifacts, lower friction Define platform primitives other products build on — bias toward generality, reliability, clean abstractions Drive measurable supply metrics: developer activation, integration velocity, artifact quality, ecosystem breadth Lead technical strategy for your platform areas: roadmap, API design, cross-team alignment Set the engineering bar: architecture, design standards, observability, operational excellence Mentor senior engineers; raise the team's technical depth What We Look For at Senior Staff Level You're a multiplier. You trace developer pain to platform gaps, design general solutions, ship them in ways that unlock categories of use cases. You raise the abstraction level of everything you touch. You shape product decisions from the technical side. Your mentees have gone on to senior roles or owned critical systems. Required: 12+ years designing, building, operating production systems; significant platform or infrastructure team experience Experience leading design and multi-year operation of developer platforms that reached 100+ external teams; can point to instances where the platform unlocked new use cases or accelerated velocity by order of magnitude Deep expertise in distributed systems: reliability, API design, performance, fault tolerance, observability Strong systems engineer with fluency in production microservices environments at scale Built general-purpose platforms other engineers depend on; understand what makes an abstraction good vs. leaky End-to-end ownership: you design, code, debug in production, iterate based on real feedback — not handing off at the design doc Navigate tradeoffs with incomplete data: know when to generalize vs. specialize, when to build vs. integrate, when to move fast vs. lock in design Understand developer activation funnels and feedback loops that turn builders into core contributors Nice to Have: Familiarity with LLM-based architectures or agent orchestration patterns Experience with enterprise developer environments: auth, sandboxing, compliance, multi-tenancy constraints 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 get to do in this role: You will design, build, and operate components of our Kubernetes platform and its core subsystems, owning a well-scoped area end to end. You will write and review a lot of Go—controllers, operators, platform services, and the automation around them. You will take a defined problem with open design questions and drive it to a working, production-ready answer, checking in on direction rather than needing it handed to you. You will help operate what the team builds: on-call, incident investigation, and the follow-up work that stops the same failure twice. You will contribute to how workloads run on the platform, working with the teams that depend on it. You will work on a platform that serves regulated markets, where compliance constraints including FedRAMP shape design choices. To be successful in this role you have: Experience leveraging or critically thinking about how to integrate AI into engineering work — whether using AI-powered coding and operational tooling, automating workflows, or reasoning about how AI changes the way software and infrastructure are built. Already built and shipped Kubernetes infrastructure or platform components in production. A smaller scope than a whole platform is fine, but it needs to be something you designed and owned, not something you configured or consumed. 5+ years building production software, with real experience running workloads on Kubernetes and debugging them when they misbehave. Solid programming skills in Go, or strong systems-language skills and a clear willingness to work primarily in Go. Working experience with at least one major hyperscaler (AWS, Azure, GCP), including its core compute, networking, and IAM primitives. Comfort with containers, CI/CD and GitOps-based delivery, and infrastructure-as-code. Curiosity about how AI changes platform engineering—using AI-powered tooling, automating operational work, or thinking about what it means for how infrastructure is built and run. It also helps if you have : Experience building Kubernetes controllers or operators with the operator pattern. Exposure to container networking (CNI), service mesh, or workload identity and mTLS. Experience with secrets management, certificate lifecycle, or similar core platform subsystems. Experience with observability tooling—metrics, tracing, and SLOs. Experience with managed Kubernetes (EKS/AKS/GKE), Terraform, or Crossplane. 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.

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.

AI Security Incubation & Innovation Security and Risk Engineering About the team The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning. This is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like. The Role As a Senior Software Engineer – AI Native Development, you will design, build, and operate next-generation AI-powered applications and platforms. You will combine strong full-stack software engineering fundamentals with hands-on expertise in modern AI/ML technologies to build production-grade experiences powered by LLMs, agents, retrieval, and intelligent automation. You will work across the technology stack—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML workflows, and cloud infrastructure. You will be expected to use AI-native development practices to accelerate engineering productivity while maintaining high standards for scalability, reliability, security, and quality. This role is ideal for an engineer who enjoys solving complex problems end-to-end and is excited about applying AI as a core engineering capability, not simply as an add-on to traditional software. What You'll Own Design and build end-to-end full-stack applications, including frontend experiences, backend services, APIs, data layers, and cloud-native infrastructure. Build production-grade AI/ML-powered capabilities using LLMs, RAG, embeddings, semantic search, agentic workflows, and intelligent decision-making. Design and implement agentic architectures, including tool calling, orchestration, planning loops, memory, context management, and failure recovery. Develop reliable AI-powered APIs and services that integrate frontier models and enterprise data securely and efficiently. Build retrieval and grounding pipelines using vector search, hybrid search, semantic retrieval, re-ranking, and contextual enrichment. Establish evaluation and observability mechanisms to measure AI quality, accuracy, latency, cost, reliability, and safety. Take features from concept and prototype through production deployment and ongoing operation, with ownership of quality and reliability. Work with product, design, platform, data, security, and other engineering teams to translate ambiguous problems into scalable technical solutions. Contribute to architecture and design decisions, code reviews, engineering standards, and technical direction. Mentor engineers and help raise the bar on full-stack engineering and production AI development practices. What You'll Do Design and develop scalable, maintainable frontend applications and backend services using REST/GraphQL APIs, microservices, event-driven services, and distributed systems. Work with modern frontend technologies such as React, TypeScript, JavaScript, or equivalent frameworks. Build cloud-native applications with strong focus on scalability, performance, reliability, and security. Own software delivery across development, testing, deployment, monitoring, and production operations. Build applications leveraging LLMs, generative AI, embeddings, RAG, semantic search, and agentic workflows. Integrate frontier AI models and SDKs such as OpenAI, Anthropic, Google, or equivalent platforms. Apply prompt engineering, structured outputs, function/tool calling, context engineering, and model selection to real-world applications. Design agent workflows that can reason, use tools, retrieve information, execute actions, and recover from failures. Build AI evaluation frameworks and automated tests to measure model and application quality. Balance model capability, accuracy, latency, scalability, and cost when selecting and integrating AI models. Apply AI safety, security, privacy, governance, and guardrail practices to production AI systems. Explore and adopt emerging AI technologies and rapidly turn promising capabilities into production-ready solutions. AI-Native Engineering Practices Use AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, Windsurf, or equivalent tools as part of the software development lifecycle. Apply AI to improve engineering productivity across coding, testing, debugging, documentation, code review, and system design. Develop effective workflows for collaborating with coding agents while maintaining engineering quality and accountability. Help establish best practices for AI-native software development across the engineering organization. 5+ years of software engineering experience building and operating production-quality software. Strong understanding of software engineering fundamentals, data structures, algorithms, design patterns, APIs, and distributed systems. Hands-on experience developing full-stack applications, with strength in both frontend and backend engineering. Strong programming experience in one or more of Python, Java, Go, TypeScript, JavaScript, or similar languages. Experience with modern frontend development, preferably React and TypeScript or equivalent technologies. Experience designing and building cloud-native applications, scalable APIs, microservices, databases, and distributed systems. Hands-on experience building or integrating AI/ML-powered applications in production or near-production environments. Practical understanding of modern AI concepts including LLMs, embeddings, RAG, vector databases/search, semantic search, agents, tool calling, and model evaluation. Experience integrating one or more frontier AI model platforms/SDKs such as OpenAI, Anthropic, or Google. Ability to take an AI/ML prototype and turn it into a reliable, scalable, maintainable production solution. Strong debugging, problem-solving, and system-design skills. Strong communication and collaboration skills with the ability to work effectively across engineering, product, design, data, and platform teams. Demonstrated ownership of technical decisions and a track record of improving code quality and engineering practices. Nice to Have Experience designing multi-agent systems and agent orchestration frameworks. Experience with AI evaluation, observability, guardrails, and responsible AI practices. Experience with vector databases, hybrid retrieval, re-ranking, knowledge graphs, or enterprise search. Experience with ML pipelines, MLOps, model monitoring, or inference optimization. Experience with cloud platforms such as AWS, Azure, or GCP. Experience with Kubernetes, containers, CI/CD, and infrastructure-as-code. Experience with AI coding agents such as Claude Code, Codex, Cursor, or Windsurf. Contributions to open-source AI/ML or developer tooling projects. Experience working on cybersecurity, identity, risk, enterprise SaaS, or other complex domain platforms is a plus. Qualifications 5+ years of software engineering experience, or equivalent practical experience. Experience designing and delivering production software systems. Experience building or integrating AI/ML-powered applications in a production or near-production environment. Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability. Strong programming experience in Python and/or Java, Go, or a similar language. Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience. Cybersecurity or security-product experience is a plus. What Success Looks Like In this role, you will be successful when you can: Build end-to-end: Take a product requirement from UI and API design through AI services, data, deployment, and production operation. Build AI-native: Understand when and how to apply LLMs, agents, retrieval, and other AI capabilities to solve problems effectively. Engineer for production: Move beyond prototypes and deliver systems that are scalable, observable, secure, reliable, and maintainable. Think across the stack: Understand the trade-offs between user experience, application architecture, data, AI models, infrastructure, cost, and performance. Raise the engineering bar: Influence architecture, mentor engineers, and establish effective practices for building AI-native software. FD21 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.