Careers at Signifyd95

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! Role Overview Signifyd uses the latest in AI and machine learning technology to give our ecommerce customers the confidence they need to do business free from fears of fraud and other forms of ecommerce abuse. In order to do this we have to continually innovate and nowhere is this more important than in our modeling space. The modeling group creates, deploys and runs the models and services that keep us a step ahead and right now we need an engineering manager to help us build our next generation technology. The successful candidate will lead a team of engineers who will partner with our AI group and engineering as a whole to help bring ideas from conception to production, while ensuring the systems we already have continue to run smoothly. It’s a role for someone who can operate comfortably on both sides of the technical/business line, contributing to technical discussions but people-focused enough to build the relationships between engineering, our AI lab, product and our risk management organization. You will work daily with data scientists, machine learning engineers, product owners and other cross-functional stakeholders whose work depends on your team's systems. Success means being a trusted translator and partner across all of these groups, not just a manager of your own team's output. Your core responsibilities are delivery, fostering innovation and people leadership. How hands-on you get as part of that is up to you but you will be expected to drive closure on technical decisions and implementation. Key Responsibilities (outcome and accountability) 1. Team Leadership & Growth Manage, coach and grow a team of engineers, providing regular feedback, career development while ensuring performance remains high. Build a healthy team culture with clear ownership, sustainable pace and high standards for quality and craftsmanship. Recruit and onboard new engineers as needed and develop technical leadership within the team. 2. Stakeholder & Relationship Management Manage competing priorities and expectations across multiple stakeholder groups, building trust through fostering mutual understanding, delivering consistently and communicating transparently. Represent the team's roadmap, capacity and risks in cross-functional planning and prioritization discussions. 3. Technical Engagement & Decision-Making Drive technical discussions and design reviews, with enough depth to understand the implications of key decisions on reliability, scalability and production outcomes. Partner with engineers on architecture and design choices, asking the right questions rather than dictating solutions. Ensure the team's technical decisions are well understood by, and defensible to, other stakeholders. 4. AI-Enabled Ways of Working Model and champion effective use of AI tools across the team's day-to-day work, including AI-assisted research, administrative workflows, and code generation. Help the team develop good judgment about how to balance the productivity gains AI can bring against the risks it introduces. Continuously look for ways AI tooling can improve team efficiency, code quality, and decision-making, and share what's working with peers. Manage effectively through the major changes AI is bringing to the industry. 5. Delivery & Operational Rigor Own delivery of the team's roadmap balancing feature work, technical debt and reliability commitments. Establish and maintain appropriate quality, testing and monitoring practices. Provide clear, proactive communication on progress, risks and blockers to stakeholders and leadership. Define metrics as needed and develop an understanding of why we did or didn’t hit our targets. Qualifications & Skills (experience / depth) Experience 5+ years working as a software engineer. Some of that experience should be in the ML sphere. This should not be your first time managing and we’d prefer at least 4 years of experience managing engineers directly in an ML focused domain. Technical & AI Fluency Comfortable engaging in technical discussions on architecture, data flows, and system design. Demonstrable experience using AI tools in a professional capacity for research, administration and code generation with a clear point of view on how to use them responsibly and cost-effectively. Strong knowledge of software development best practices, cloud computing platforms, big data technologies, MLOps toolkits, and SLO-driven reliability management. Experience working with at least one of Databricks, Spark, Airflow, Vertex and the GCP technology stack in general is preferred. Stakeholder Management & Communication Strong track record managing relationships with a diverse set of technical and non-technical stakeholders. Ability to communicate clearly across audiences and influence without authority. Comfortable navigating competing priorities and pushing back constructively when needed. Leadership Genuine care for engineer growth and development, with experience giving structured feedback and building career paths. Ability to build trust and psychological safety within the team while holding a high bar for output and accountability. #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $210,000 – $235,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $205,000 – $230,000 annually Tier 3 (US - All Other): $200,000 – $225,000 annually Equity: This role is eligible for a stock option grant of 3,000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd provides a base salary, bonus, equity and benefits to all its employees. Our posted job may span more than one career level, and offered level and salary will be determined by the applicant’s specific experience, knowledge, skills, abilities, and location, as well as internal equity and alignment with market data. USA Base Salary Pay Range $200,000 — $235,000 USD Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! ML Engineering Manager Department: AI Lab/Machine Learning Signifyd AI Lab (SAIL) builds the ML products behind Signifyd's fraud and risk decisions. We improve the predictive performance of the models that decide e-commerce transactions at scale, we scale the ML capabilities of our Risk organization, and we push into the new markets and problem spaces that expand the market Signifyd can sell to. Every space in this department is a mix of experimentation, code, and statistics. We don't create walls between the people who have the ideas and the people who build them. The team splits its time between near-term continuous model improvements and longer-horizon innovation bets to improve the company’s capabilities in 2027 and beyond. These bets surface from the ground up in an environment where we believe those closest to the problems are best placed to understand how to solve them. We’re hiring a manager to lead one of the teams in this department. Who You Are You are a hands-on Player-Coach who thrives in ambiguity—where the roadmap is a set of hypotheses, and the answer to "will this work?" is "we'll know in three weeks." You bring: Technical Credibility (The "Player"): You stay close enough to the work to have a grounded opinion. You read the code, inspect evaluation pipelines, and can immediately tell the difference between a statistical result that will hold up in production and one that just happened to look good on a single test window. Leadership & Rigor (The "Coach"): You hold a high bar for evidence without becoming a bottleneck to experimentation. You mentor engineers to own their code quality, and you translate complex ML performance metrics into clear business outcomes for Risk leadership. Executive Judgment: You know how to balance research bets against quarterly delivery, disagree and commit when decisions are made, and build an environment where well-documented negative experimental results are celebrated as real progress. Responsibilities: Lead and grow the team Guide career development, mentor the team, manage conflicts, and nurture a positive, collaborative environment across a geographically distributed organization. Engage in regular 1:1s, give constant feedback, and create a safe environment for open discussion — including the discussions that follow an experiment that didn't work. Encourage a culture of learning and improvement, provide technical guidance, and support team members in both technical and soft skills. Identify and address gaps in team capabilities and processes to enhance team efficiency and success. Run a portfolio of experiments, not a delivery queue Partner with your tech leads, who own and drive the technical roadmap for their areas. Your job is not to be the sole source of ideas — it is to pressure-test them, sharpen them, make sure the strongest ones get resourced, and make sure the people generating them have the room and the support to do it. Make the calls the roadmap can't make for you: which hypotheses get compute and headcount, which get another iteration, and which get a clear, documented "no." A well-run negative result is a real outcome, and we treat it as one Manage the trade-off between a committed improvement target you must hit this year and research bets that may not pay off for several quarters. You will re-cut that budget as evidence arrives, and you'll be able to explain the reasoning to both your team and your stakeholders. Bring rigor to how the team decides something worked. Offline results have to predict online behavior; a strong point estimate on a single evaluation window is a starting point, not a conclusion. Own delivery on a cadence. Independent experimental workstreams have to converge into a release candidate, get evaluated end to end, and ship — including the hard call to leave a workstream out of a release when it isn't carrying its weight. Set direction from data, in partnership with Risk Work directly with our Risk partners as your primary stakeholders. Our commitments to them are explicit, measured, and written down; we deliver model performance, and they own thresholds, rules, and how decisions are applied to merchants. Operate with a high degree of autonomy. Our direction comes from measured performance against those commitments and from what our own experiments tell us, not from a product backlog handed to the team. You are expected to know what your team should be working on and to defend it, rather than wait to be told. Partner with our platform and infrastructure engineering teams on the feature systems, training pipelines, and experimentation tooling your team depends on — and be clear about where the boundary sits between what SAIL should own and what belongs to Engineering. Represent your team's results to a broad audience: engineering leadership, Risk leaders, and the wider company. Requirements: Roughly 5+ years in machine learning, data science, or ML-adjacent software engineering, including at least 3 years of people management — guiding career development, addressing conflicts, and building a healthy, high-performing team. Genuine depth in at least one of engineering and applied statistics, and real working competence in the other. We are not hiring a manager of analysts, and we are not hiring a manager of a pure software team. Our engineers train production models that decide serious traffic, and we expect their manager to be able to engage with that work at a technical level. Demonstrated ability to lead work under real uncertainty: setting a direction when the answer isn't known yet, changing course when evidence says to, and communicating both without eroding your team's confidence. Excellent written and verbal communication. Much of our decision-making happens in documents, and we expect managers to write well. Autonomy in recognizing priorities and evaluating the impact of outcomes, and comfort working without close supervision in a fast-moving environment. Commitment to quality. You take pride in work that excels in correctness, reproducibility, and reliability, and you set that standard for your team. Candidates must be based in Hungary #LI-Hybrid Benefits: Stock Options Annual Performance Bonus or Commissions Pension matched up to 3% ‘Day one’ access to great health insurance scheme Paid team social events Mental wellbeing resources Dedicated learning budget through Learnerbly Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! Signifyd AI Lab (SAIL) builds the ML products behind Signifyd's fraud and risk decisions. We improve the predictive performance of the models that decide e-commerce transactions at scale, we scale the ML capabilities of our Risk organization, and we push into the new markets and problem spaces that expand the market Signifyd can sell to. Every space in this department is a mix of experimentation, code, and statistics. We don't create walls between the people who have the ideas and the people who build them. The team splits its time between near-term continuous model improvements and longer-horizon innovation bets to improve the company’s capabilities in 2027 and beyond. These bets surface from the ground up in an environment where we believe those closest to the problems are best placed to understand how to solve them. We’re hiring a manager to lead one of the teams in this department. Who You Are You are a hands-on Player-Coach manager who thrives in ambiguity—where the roadmap is a set of hypotheses, and the answer to "will this work?" is "we'll know in three weeks." You bring: Technical Credibility (The "Player"): You stay close enough to the work to have a grounded opinion. You read the code, inspect evaluation pipelines, and can immediately tell the difference between a statistical result that will hold up in production and one that just happened to look good on a single test window. Leadership & Rigor (The "Coach"): You hold a high bar for evidence without becoming a bottleneck to experimentation. You mentor engineers to own their code quality, and you translate complex ML performance metrics into clear business outcomes for Risk leadership. Executive Judgment: You know how to balance research bets against quarterly delivery, disagree and commit when decisions are made, and build an environment where well-documented negative experimental results are celebrated as real progress. What You'll Do Lead and grow the team Guide career development, manage conflicts, and nurture a positive work environment. Develop career plans with team members, provide guidance on skill development, and follow up on their evolution. Engage in regular 1:1s, give constant feedback, and create a safe environment for open discussion — including the discussions that follow an experiment that didn't work. Set clear goals, mentor the team, and foster a collaborative environment across a geographically distributed organization. Encourage a culture of learning and improvement, provide technical guidance, and support team members in both technical and soft skills. Conduct technical and hiring-manager interviews, train the team on interviewing techniques, and help us keep raising the bar as we grow. Identify and address gaps in team capabilities and processes to enhance team efficiency and success. Run a portfolio of experiments, not a delivery queue Partner with your tech leads, who own and drive the technical roadmap for their areas. Your job is not to be the sole source of ideas — it is to pressure-test them, sharpen them, make sure the strongest ones get resourced, and make sure the people generating them have the room and the support to do it. When you do bring an idea, you bring it as a peer in the technical conversation. Make the calls the roadmap can't make for you: which hypotheses get compute and headcount, which get another iteration, and which get a clear, documented "no." A well-run negative result is a real outcome, and we treat it as one — but only if it's declared, written down, and learned from. Manage the trade-off between a committed improvement target you must hit this year and research bets that may not pay off for several quarters. You will re-cut that budget as evidence arrives, and you'll be able to explain the reasoning to both your team and your stakeholders. Bring rigor to how the team decides something worked. Offline results have to predict online behavior; a strong point estimate on a single evaluation window is a starting point, not a conclusion. You will be the person asking whether the improvement survives a rolling evaluation, whether it's already captured by a change we shipped last month, and what would have to be true for it to be wrong. Own delivery on a cadence. Independent experimental workstreams have to converge into a release candidate, get evaluated end to end, and ship — including the hard call to leave a workstream out of a release when it isn't carrying its weight. Set direction from data, in partnership with Risk Work directly with our Risk partners as your primary stakeholders. Our commitments to them are explicit, measured, and written down; we deliver model performance, and they own thresholds, rules, and how decisions are applied to merchants. Operate with a high degree of autonomy. Our direction comes from measured performance against those commitments and from what our own experiments tell us, not from a product backlog handed to the team. You are expected to know what your team should be working on and to defend it, rather than wait to be told. Partner with our platform and infrastructure engineering teams on the feature systems, training pipelines, and experimentation tooling your team depends on — and be clear about where the boundary sits between what SAIL should own and what belongs to Engineering. Represent your team's results to a broad audience: engineering leadership, Risk leaders, and the wider company. What You'll Need Roughly 5+ years in machine learning, data science, or ML-adjacent software engineering, including at least 3 years of people management — guiding career development, addressing conflicts, and building a healthy, high-performing team. Genuine depth in at least one of engineering and applied statistics, and real working competence in the other. We are not hiring a manager of analysts, and we are not hiring a manager of a pure software team. Our engineers train production models that decide serious traffic, and we expect their manager to be able to engage with that work at a technical level. Demonstrated ability to lead work under real uncertainty: setting a direction when the answer isn't known yet, changing course when evidence says to, and communicating both without eroding your team's confidence. Excellent written and verbal communication. Much of our decision-making happens in documents, and we expect managers to write well. Autonomy in recognizing priorities and evaluating the impact of outcomes, and comfort working without close supervision in a fast-moving environment. Commitment to quality. You take pride in work that excels in correctness, reproducibility, and reliability, and you set that standard for your team. #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $220,000 – $245,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $210,000 – $235,000 annually Tier 3 (US - All Other): $200,000 – $225,000 annually Equity: This role is eligible for a stock option grant of 5,000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! The Applied Decision Science (ADS) team builds production ML models and risk management tools that are the core of Signifyd's product. We help businesses of all sizes minimize their fraud exposure and grow their sales. We improve the e-commerce shopping experience for everyone by reducing the friction experienced by good buyers and blocking fraudulent purchase attempts. ADS builds and manages the entire decision stack - from designing and deploying the ML models that assess the riskiness of a transaction, to building the tools the Risk team uses to manage and fight fraud. We seek to standardize and automate repetitive work so we can spend more time on experiments and high-leverage projects. We value collaboration and team ownership. Data scientists in Signifyd are true “full stack” operators, requiring knowledge of how transaction information received via our API traverses its way through our system and into the models we are responsible for building. When you test a hypothesis at Signifyd, you’re responsible for the end-to-end development, deployment, and evaluation process. This is a massive responsibility, and no one should feel like they're solving a hard problem alone. Together we help each other develop our skillsets through peer review of experiments and code, group paper study to deepen our machine learning and statistical understanding, and frequent knowledge-sharing through live demos, write-ups, and cross-team projects. All team members are expected and encouraged to weigh in as an external reviewer on a peer's idea or approach, regardless of level. A couple quick notes on the Signifyd culture: We’re no stranger to remote work. Most of our workforce (ICs and leaders) are primarily remote. We tend to gather individual teams together once a year. There is no travel requirement for this role. We are heavy Slack users. We are heavy users of generative AI tools. We dislike token-maxxing, but enjoy the expansion of capabilities that have come with genAI. We ask that during the interview you don’t use genAI, as we want to know what you know. Responsibilities: Partner with the Business Unit Lead and their merchant portfolio to identify gaps in decisioning performance and implement solutions, with guidance from senior team members. Utilize existing, or build net new production machine learning models that identify fraud, in collaboration with other data scientists and machine learning engineers. Identify and build automation that reduces repetitive manual work. Run experiments to identify optimal decisioning strategies, balancing complexity and performance. Communicate complex ideas to a variety of audiences, from Customer Success and Sales, to limited interactions with external customers. Write production and offline analytical code in Python. Work with distributed data pipelines in Spark/Databricks/GCP. Requirements: A degree in computer science or a comparable analytical field. 3+ years of post-undergrad work experience required. Strong verbal and written communication skills. Strong machine learning and statistical background. Write code and review others' in a shared codebase in Python. Practical SQL knowledge. Design experiments and collect data. Experience with distributed analytics and data tooling such as Spark and Databricks. This role has on-call shifts, as part of our weekend rotation, Fri/Sat/Sun. While the number of shifts is subject to change, currently it works out to about six weekends a year. Nice to Have: Previous work in fraud, payments, or e-commerce. Data analysis in a distributed environment. A passion for writing well-tested production-grade code. Experience with AI coding agents and automation. Experience of running A/B tests in production environments. An advanced degree. #LI-Remote Our UK benefits: Stock Options Annual Performance Bonus or Commissions Pension matched up to 8% ‘Day one’ access to great health, dental and optical insurance scheme Generous annual leave plus public holidays Cycle to Work Scheme Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads, plus 12 weeks half-pay for mums) Regular paid social events organized by our social committee Mental wellbeing resources Dedicated learning budget through Learnerbly We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships. We also want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! The Applied Decision Science (ADS) team builds production ML models and risk management tools that are the core of Signifyd's product. We help businesses of all sizes minimize their fraud exposure and grow their sales. We improve the e-commerce shopping experience for everyone by reducing the friction experienced by good buyers and blocking fraudulent purchase attempts. ADS builds and manages the entire decision stack - from designing and deploying the ML models that assess the riskiness of a transaction, to building the tools the Risk team uses to manage and fight fraud. We seek to standardize and automate repetitive work so we can spend more time on experiments and high-leverage projects. We value collaboration and team ownership. Data scientists in Signifyd are true “full stack” operators, requiring knowledge of how transaction information received via our API traverses its way through our system and into the models we are responsible for building. When you test a hypothesis at Signifyd, you’re responsible for the end-to-end development, deployment, and evaluation process. This is a massive responsibility, and no one should feel like they're solving a hard problem alone. Together we help each other develop our skillsets through peer review of experiments and code, group paper study to deepen our machine learning and statistical understanding, and frequent knowledge-sharing through live demos, write-ups, and cross-team projects. All team members are expected and encouraged to weigh in as an external reviewer on a peer's idea or approach, regardless of level. A couple quick notes on the Signifyd culture: We’re no stranger to remote work. Most of our workforce (ICs and leaders) are primarily remote. We tend to gather individual teams together once a year. There is no travel requirement for this role. We are heavy Slack users. We are heavy users of generative AI tools. We dislike token-maxxing, but enjoy the expansion of capabilities that have come with genAI. We ask that during the interview you don’t use genAI, as we want to know what you know. Responsibilities: Partner with the Business Unit Lead and their merchant portfolio to identify gaps in decisioning performance and implement solutions, with guidance from senior team members. Utilize existing, or build net new production machine learning models that identify fraud, in collaboration with other data scientists and machine learning engineers. Identify and build automation that reduces repetitive manual work. Run experiments to identify optimal decisioning strategies, balancing complexity and performance. Communicate complex ideas to a variety of audiences, from Customer Success and Sales, to limited interactions with external customers. Write production and offline analytical code in Python. Work with distributed data pipelines in Spark/Databricks/GCP. Requirements: A degree in computer science or a comparable analytical field. 3+ years of post-undergrad work experience required. Strong verbal and written communication skills. Strong machine learning and statistical background. Write code and review others' in a shared codebase in Python. Practical SQL knowledge. Design experiments and collect data. Experience with distributed analytics and data tooling such as Spark and Databricks. This role has on-call shifts, as part of our weekend rotation, Fri/Sat/Sun. While the number of shifts is subject to change, currently it works out to about six weekends a year. Nice to Have: Previous work in fraud, payments, or e-commerce. Data analysis in a distributed environment. A passion for writing well-tested production-grade code. Experience with AI coding agents and automation. Experience of running A/B tests in production environments. An advanced degree. #LI-Remote Our UK benefits: Stock Options Annual Performance Bonus or Commissions Pension matched up to 8% ‘Day one’ access to great health, dental and optical insurance scheme Generous annual leave plus public holidays Cycle to Work Scheme Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads, plus 12 weeks half-pay for mums) Regular paid social events organized by our social committee Mental wellbeing resources Dedicated learning budget through Learnerbly We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships. We also want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! The Applied Decision Science (ADS) team builds production ML models and risk management tools that are the core of Signifyd's product. We help businesses of all sizes minimize their fraud exposure and grow their sales. We improve the e-commerce shopping experience for everyone by reducing the friction experienced by good buyers and blocking fraudulent purchase attempts. ADS builds and manages the entire decision stack - from designing and deploying the ML models that assess the riskiness of a transaction, to building the tools the Risk team uses to manage and fight fraud. We seek to standardize and automate repetitive work so we can spend more time on experiments and high-leverage projects. We value collaboration and team ownership. Data scientists in Signifyd are true “full stack” operators, requiring knowledge of how transaction information received via our API traverses its way through our system and into the models we are responsible for building. When you test a hypothesis at Signifyd, you’re responsible for the end-to-end development, deployment, and evaluation process. This is a massive responsibility, and no one should feel like they're solving a hard problem alone. Together we help each other develop our skillsets through peer review of experiments and code, group paper study to deepen our machine learning and statistical understanding, and frequent knowledge-sharing through live demos, write-ups, and cross-team projects. All team members are expected and encouraged to weigh in as an external reviewer on a peer's idea or approach, regardless of level. A couple quick notes on the Signifyd culture: We’re no stranger to remote work. Most of our workforce (ICs and leaders) are primarily remote. We tend to gather individual teams together once a year. There is no travel requirement for this role. We are heavy Slack users. We are heavy users of generative AI tools. We dislike token-maxxing, but enjoy the expansion of capabilities that have come with genAI. We ask that during the interview you don’t use genAI, as we want to know what you know. Responsibilities: Partner with the Business Unit Lead and their merchant portfolio to identify gaps in decisioning performance and implement solutions, with guidance from senior team members. Utilize existing, or build net new production machine learning models that identify fraud, in collaboration with other data scientists and machine learning engineers. Identify and build automation that reduces repetitive manual work. Run experiments to identify optimal decisioning strategies, balancing complexity and performance. Communicate complex ideas to a variety of audiences, from Customer Success and Sales, to limited interactions with external customers. Write production and offline analytical code in Python. Work with distributed data pipelines in Spark/Databricks/GCP. Requirements: A degree in computer science or a comparable analytical field. 3+ years of post-undergrad work experience required. Strong verbal and written communication skills. Strong machine learning and statistical background. Write code and review others' in a shared codebase in Python. Practical SQL knowledge. Design experiments and collect data. Experience with distributed analytics and data tooling such as Spark and Databricks. This role has on-call shifts, as part of our weekend rotation, Fri/Sat/Sun. While the number of shifts is subject to change, currently it works out to about six weekends a year. Nice to Have: Previous work in fraud, payments, or e-commerce. Data analysis in a distributed environment. A passion for writing well-tested production-grade code. Experience with AI coding agents and automation. Experience of running A/B tests in production environments. An advanced degree. #LI-Remote Our UK benefits: Stock Options Annual Performance Bonus or Commissions Pension matched up to 8% ‘Day one’ access to great health, dental and optical insurance scheme Generous annual leave plus public holidays Cycle to Work Scheme Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads, plus 12 weeks half-pay for mums) Regular paid social events organized by our social committee On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships. We also want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! Signifyd is looking to hire a Staff Software Engineer I for its Platform Engineering group. Within our broader Engineering organization, this pivotal role will set the technical vision and long-term strategy for our enterprise cloud infrastructure, driving its design and evolution across multiple engineering teams. You will provide technical leadership that extends beyond the Platform team, partnering with engineering leaders, peers, and cross-functional stakeholders to shape how Signifyd builds, automates, and scales the vast computing environment that supports our core decision platform for customers. This includes championing how AI and intelligent automation get built into the platform like self-healing infrastructure and AI-assisted incident response to the reliable, scalable systems that host Signifyd's own platform in production. Thriving in an innovative culture of rapid iteration and continuous learning, you will operate with significant autonomy in organization wide problem spaces, ensuring technical decisions align with while actively shaping strategic business outcomes. To ensure seamless system operations at scale, the ideal candidate will possess a strong software development skillset, exceptional analytical and communication skills, and deep technical proficiency across cloud technologies, containers, Kubernetes, cloud networking, security, and platform engineering. Fluency in applying AI and machine learning to platform problems such as intelligent automation, anomaly detection, and AI-assisted developer tooling. The knowledge of the infrastructure needed to run AI/ML workloads reliably at scale is a strong plus, along with a demonstrated track record of driving initiatives that span multiple teams. About Signifyd Signifyd protects ecommerce businesses from fraud and abuse with a 100% financial guarantee, using machine learning, big data, and human expertise to help merchants sell more, safely. We work with customers ranging from emerging startups to large-scale enterprises processing millions of transactions, and there's a lot more ground to cover — which means real ownership and real impact for the people building our platform. About Platform Engineering Platform Engineering builds and operates the foundational systems that power Signifyd globally — cloud infrastructure, containers and Kubernetes, cloud networking, and security — along with the self-healing automation that keeps our core decision platform running reliably at scale. The team is also responsible for building AI and intelligent automation directly into the platform, from AI-assisted incident response to the infrastructure that reliably runs Signifyd's own AI/ML workloads in production. What You’ll Do As a Staff Software Engineer in Platform Engineering capability, you will play a key leadership role in setting the technical vision and long-term strategy for Signifyd's enterprise cloud infrastructure. You will decide how Signifyd automates, scales, and secures the computing environment behind our core decision platform. You will partner with engineering leaders, Staff+ peers, and cross-functional stakeholders to turn ambiguous, org-wide problems into concrete technical direction, operating with significant autonomy well beyond the scope of any single team. You will also help bring AI and machine learning into how the platform runs itself — intelligent automation, anomaly detection, AI-assisted developer tooling, and the infrastructure needed to run AI/ML workloads reliably at scale. Pioneer AI-Driven Infrastructure Build the future: Define the architectural strategy for integrating LLM-powered, agentic tools across the platform. Set the technical direction for self-healing infrastructure and automated root-cause analysis, influencing multiple teams' roadmaps to cut operational toil and speed up incident resolution. Architect for Scale, Security & Reliability Evolve the ecosystem: Own the multi-quarter roadmap for our GCP and Kubernetes-based cloud capabilities, balancing developer feedback with emerging cloud technologies and building consensus among senior stakeholders. Drive operational excellence: Set the standard for SLOs and incident management across the platform, and share ownership of platform health through on-call rotations — using those frontline insights to harden systems at scale. Embed security: Architect access controls, encryption, and vulnerability management as a foundational platform standard that keeps the platform secure without slowing developer velocity. Optimize footprint: Set the strategic direction for cloud capacity management and FinOps, delivering efficient scaling and cost transparency that other teams adopt as the standard. Elevate Developer Experience (DevEx) Productize the platform: Own the technical strategy for turning CI/CD (GitHub Actions, TeamCity), deployment (ArgoCD), and build tooling into a cohesive, self-service product — driving Change Failure Rate toward zero across engineering. Pave the golden path: Define the platform standards built directly into our tooling, so the right way is automatically the fastest and easiest way for every engineer at Signifyd. Technical Leadership & Mentorship Solve systemic problems: Serve as the escalation point for the most complex, ambiguous infrastructure challenges, delivering durable fixes and shaping how other teams approach similar problems. Champion the culture: Foster a customer-focused engineering culture, mentor engineers at every level, raise the technical bar, and advocate for product-thinking and empathy for the developer experience. What You'll Bring Cloud & Infrastructure Mastery Cloud Platform: Deep, demonstrated mastery of Google Cloud Platform (GCP); AWS experience is a strong plus. Expert-level knowledge of cloud networking, protocols, and security best practices, with a track record of setting standards others follow. Kubernetes: Extensive expertise in architecting and operating self-managed and cloud-managed Kubernetes environments at scale, including setting Kubernetes strategy across multiple teams. Data Systems: Proven experience in architecting and scaling data storage and streaming technologies (e.g., PostgreSQL, MySQL, Cassandra, Elasticsearch, Kafka) in ways that shape platform-wide standards. Software Engineering & DevEx Coding Proficiency: Strong software development skills in high-level languages (e.g., Python, Go, Java, TypeScript), with a history of setting engineering practices adopted beyond your immediate team. IaC & GitOps: Demonstrated ability to architect secure, self-service infrastructure using Infrastructure-as-Code (Terraform) and declarative CI/CD pipelines (ArgoCD, GitHub Actions, TeamCity), driving down Change Failure Rate across teams. Observability & AI Fluency AI Tooling: Proficiency with modern AI-assisted development tools (Cursor, GitHub Copilot, Claude), paired with the critical judgment to audit AI-generated code for security, correctness, and long-term maintainability — and to set guidelines for how teams adopt these tools responsibly. Telemetry: Experience architecting scalable, OpenTelemetry-based monitoring and tracing that helps engineering teams quickly pinpoint bottlenecks and lower MTTR. Vision & Delivery Architectural Mindset: Deep, cross-domain expertise in cloud security, cost optimization (FinOps), and performance efficiency, with a demonstrated ability to translate that expertise into technical strategy. Execution: A track record of driving complex, cross-team technical initiatives to completion in a fast-paced, collaborative environment, with minimal oversight. #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $200,000 - 225,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $195,000- $220,000 annually Tier 3 (US - All Other): $190,000- $215,000 annually Equity: This role is eligible for a stock option grant of 6,000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! Department: Engineering The Engineering organization at Signifyd is at the forefront of building solutions that protect and empower global commerce. We pride ourselves on delivering highly scalable, robust systems that evolve with our customers' needs, from emerging startups to enterprise-level organizations processing millions of transactions. Our engineers thrive in an innovative environment where rapid iteration and continuous learning drive our success. We embrace modern development practices, leverage cloud-native technologies, and maintain a customer-obsessed mindset to ensure our solutions anticipate tomorrow's challenges. Strong partnerships with Product and Data Science ensure every technical decision is aligned with delivering maximum value and driving meaningful business outcomes. The Role We are looking for a staff back-end engineer to set the technical direction for the platform retailers use to integrate with Signifyd. As a member of the Integrations & Onboarding team, you will own the architecture and long-term health of our public-facing API platform and ingestion services, defining both what we build and how we build it. This role suits an engineer who is drawn to open-ended problems spanning several teams, who can turn a complicated problem space into simple and durable foundations, and whose influence shows up in other teams' roadmaps as much as in their own systems. Responsibilities Define and deliver the multi-year technical vision for our integration and ingestion platform across multiple teams and competing constraints. Make the architecture-level technology calls, including build-versus-buy decisions and framework selection, and own the tradeoffs behind them. Own the overall health and engineering quality of the platform: drive health reviews, curate testing strategy, and ensure the systems stay maintainable by engineers with varying levels of expertise. Design systems under significant ambiguity, particularly those that many other systems depend on. Partner with product management, data engineering, and other engineering teams, influencing their roadmaps where a shared solution serves the business better than a locally optimal one. Raise the technical bar across related areas through design and code review, with particular attention to cross-team interfaces and API quality. Proactively identify areas of technical debt and drive the strategy for addressing them. Serve as a role model and mentor for engineers across the organization, and champion responsible adoption of AI development tools. Requirements Bachelor's degree or equivalent practical experience. Significant experience in software development with modern programming languages, experience programming in a functional style. Track record of setting technical direction across multiple teams, and of carrying large, ambiguous initiatives through to completion. Depth in a platform or product domain that translated into distinctive business value, rather than breadth alone. Strong software design and architecture skills, including reducing complex problems to simple foundational components and designing interfaces that are difficult to misuse. Experience owning the long-term health of a significant system: testing strategy, operability, and reducing key-person dependency. Experience building distributed systems or public-facing/platform APIs at scale. Experience with cloud platforms such as AWS or GCP. Ability to influence without authority, and to communicate effectively with engineers and non-engineers alike. You use modern AI development tools fluently and help others do the same, championing responsible adoption while holding the line on quality. This means practical experience with AI coding assistants, understanding how to provide effective context, critically evaluating generated code for correctness, security, and maintainability, and coaching others toward the same standard. Our Stack Java, SQL, gRPC, HTTP/REST Kubernetes, Docker, Linux Cassandra, Pub/Sub, Bigtable, Databricks GCP, AWS #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $200,000 - 225,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego): $195,000- $220,000 annually Tier 3 (US - All Other): $190,000- $215,000 annually Equity: This role is eligible for a stock option grant of 6000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! The Senior Machine Learning Engineer will join our ML team. This team is responsible for building, maintaining, and monitoring the production ML models and offline experimentation frameworks that are at the core of Signifyd’s product. This includes the core fraud detection model that decides the majority of our traffic, alongside our model training and evaluation infrastructure. We work closely with Platform Engineering teams to contribute novel modeling methods, advanced feature engineering, and robust statistical practices. Our Culture We value tenacity, curiosity, and a hunger for learning. Our adversaries are highly motivated fraudsters looking to exploit any gap. We seek equally motivated individuals who are passionate about keeping our customers safe while pulling the field of adversarial machine learning forward. The Role As a Senior Machine Learning Engineer , you will be a driver of technical execution within the ML team. You won’t just build models—you’ll own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to deployment to production. You will be responsible for improving model performance, refining our experimentation processes, and ensuring our fraud detection systems are robust, scalable, and scientifically sound. Responsibilities: Expand ML Capabilities – Identify, prototype, and integrate new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability. Enable High-Velocity Experimentation – Own the design and implementation of ML pipeline components that accelerate our innovation Collaborate Across Functions – Partner with Product, Engineering, and Risk teams to translate business requirements into technical solutions and ensure ML initiatives align with customer needs. Raise the Bar – Foster a culture of technical excellence by championing best practices in testing, documentation, model monitoring, and development. Requirements: Education: A degree in Computer Science, Statistics, or a comparable quantitative field. Experience: 4-6+ years of post-undergrad work experience in a production-grade ML environment. Technical Depth: Strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale. Execution Focus: Proven track record of taking ML projects from research/prototype to high-scale production environments. Communication: Ability to communicate technical findings clearly to both technical peers and non-technical stakeholders. Tech Stack: Proficiency in Python , SQL, key ML libraries, and Spark Mindset: A strong outcome-oriented mindset—you care about the "why" behind the models and the business impact they create. Attention to detail is critical in fraud prevention. To demonstrate this, please start your response to the first application question with the word 'Stochastic' Nice to have: Previous experience in fraud, fintech, payments, or e-commerce. Passion for writing well-tested production-grade code A Master’s Degree or PhD. Why Join Us? Make an Impact – Your work will directly shape the future of fraud prevention, protecting billions of payments. Lead & Grow – Drive high-visibility initiatives and develop leadership skills in a fast-paced, high-growth environment. Innovate at Scale – Work with cutting-edge ML technologies and experiment freely to push the boundaries of what’s possible. Collaborative Culture – Join a team that values curiosity, ownership, and continuous learning. #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $160,000 - $190,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego):$150,000 - $180,000 annually Tier 3 (US - All Other): $140,000 - $170,000 annually Equity: This role is eligible for a stock option grant of 4,000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy. Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud. Learn about our company values here ! The Senior Machine Learning Engineer will join our ML team. This team is responsible for building, maintaining, and monitoring the production ML models and offline experimentation frameworks that are at the core of Signifyd’s product. This includes the core fraud detection model that decides the majority of our traffic, alongside our model training and evaluation infrastructure. We work closely with Platform Engineering teams to contribute novel modeling methods, advanced feature engineering, and robust statistical practices. Our Culture We value tenacity, curiosity, and a hunger for learning. Our adversaries are highly motivated fraudsters looking to exploit any gap. We seek equally motivated individuals who are passionate about keeping our customers safe while pulling the field of adversarial machine learning forward. The Role As a Senior Machine Learning Engineer , you will be a driver of technical execution within the ML team. You won’t just build models—you’ll own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to deployment to production. You will be responsible for improving model performance, refining our experimentation processes, and ensuring our fraud detection systems are robust, scalable, and scientifically sound. Responsibilities: Expand ML Capabilities – Identify, prototype, and integrate new ML technologies and infrastructure to enhance fraud detection effectiveness and scalability. Enable High-Velocity Experimentation – Own the design and implementation of ML pipeline components that accelerate our innovation Collaborate Across Functions – Partner with Product, Engineering, and Risk teams to translate business requirements into technical solutions and ensure ML initiatives align with customer needs. Raise the Bar – Foster a culture of technical excellence by championing best practices in testing, documentation, model monitoring, and development. Requirements: Education: A degree in Computer Science, Statistics, or a comparable quantitative field. Experience: 4-6+ years of post-undergrad work experience in a production-grade ML environment. Technical Depth: Strong foundation in machine learning theory, statistical evaluation, and experience with supervised/unsupervised learning at scale. Execution Focus: Proven track record of taking ML projects from research/prototype to high-scale production environments. Communication: Ability to communicate technical findings clearly to both technical peers and non-technical stakeholders. Tech Stack: Proficiency in Python , SQL, key ML libraries, and Spark Mindset: A strong outcome-oriented mindset—you care about the "why" behind the models and the business impact they create. Attention to detail is critical in fraud prevention. To demonstrate this, please start your response to the first application question with the word 'Stochastic' Nice to have: Previous experience in fraud, fintech, payments, or e-commerce. Passion for writing well-tested production-grade code A Master’s Degree or PhD. Why Join Us? Make an Impact – Your work will directly shape the future of fraud prevention, protecting billions of payments. Lead & Grow – Drive high-visibility initiatives and develop leadership skills in a fast-paced, high-growth environment. Innovate at Scale – Work with cutting-edge ML technologies and experiment freely to push the boundaries of what’s possible. Collaborative Culture – Join a team that values curiosity, ownership, and continuous learning. #LI-Remote Benefits in our US offices: Discretionary Time Off Policy (Unlimited!) 401K Match Stock Options Annual Performance Bonus or Commissions Paid Parental Leave (12 weeks) On-Demand Therapy for all employees & their dependents Dedicated learning budget through Learnerbly Health Insurance Dental Insurance Vision Insurance Flexible Spending Account (FSA) Short Term and Long Term Disability Insurance Life Insurance Company Social Events Signifyd Swag Compensation: In the United States, each work location is assigned a specific pay zone, which determines the salary range for a given position. The starting base salary for the selected candidate will be based on a variety of factors, including job-related skills, experience, qualifications, geographic location, and current market conditions. Base Salary Ranges by Pay Zone: Tier 1 (NYC/SF Bay Area/Seattle): $160,000 - $190,000 annually Tier 2 (DC Metro/Austin/Chicago/Denver/Boston/Los Angeles/San Diego):$150,000 - $180,000 annually Tier 3 (US - All Other): $140,000 - $170,000 annually Equity: This role is eligible for a stock option grant of 4,000 stock options, based on the position level and internal compensation guidelines. Bonus: This role is eligible for an annual performance bonus of up to 10% of base salary. We want to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. Signifyd's Applicant Privacy Notice