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Abuse Research Engineer

Stripe · Remote · 8611 Security Analytics

Posted Sep 25, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a novel attack vector you found and validated. How did you prove it was real and not a theory?

What they are checking: Whether they confirm findings with evidence before raising alarms that pull other teams off their work.

Sometimes the root cause of fraud is a product condition, not a bad actor's skill. Can you give me an example of that kind of finding?

What they are checking: Whether they look past individual attackers to the design choices that keep making abuse easy.

Tell me about a time an investigation went nowhere. How did you decide to stop and what did you keep from it?

What they are checking: Whether they manage research time well and still extract value from dead ends.

What would you do

Instead of waiting for alerts, you're asked to hunt for the next big fraud pattern on a Stripe product. How do you pick where to start?

What they are checking: Whether they can form a sharp hypothesis about where abuse is likely, rather than wandering through data.

When you write a threat advisory, how do you make sure the product team actually acts on it?

What they are checking: Whether they turn research into a clear, prioritized recommendation that busy engineers can execute.

You find something serious, but you can only partly confirm it. Do you raise it now or keep digging?

What they are checking: Whether they can weigh the cost of a false alarm against the cost of a slow warning.

About the work

Walk me through how you'd map an end-to-end abuse path, from how an attacker gets an account to how they cash out.

What they are checking: Whether they think like an adversary across the whole chain, which shows where a single control breaks the most.

How would you use agentic or automated testing tools to simulate attacker behavior against a product?

What they are checking: Whether they can scale their research with automation instead of testing every idea by hand.

What data sources would you pull together to understand an adversary group working across multiple Stripe products?

What they are checking: Whether they know how to link accounts and behavior across signals to see a coordinated operation.

How do you turn a finding into a regression scenario that keeps testing for that attack after it's fixed?

What they are checking: Whether they close the loop so the same vulnerability can't quietly reopen later.

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The full job description

As published by Stripe.

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Abuse Research Group (ARG) handles proactive threat hunting and adversary behavior analysis across Stripe products. Rather than reacting to alerts, the team maps end-to-end fraud and abuse paths, validates novel attack vectors, and identifies product conditions that enable fraud. Using agentic automated testing and simulation tools, ARG translates research into actionable threat advisories, strategic control recommendations, and regression scenarios to systematically eliminate vulnerabilities. What you’ll do As an Abuse Research Engineer in the Abuse Research Group, you will play a critical role in safeguarding Stripe’s financial ecosystem by proactively hunting for advanced threats, dissecting complex fraud vectors, and extracting actionable adversary intelligence.  Rather than relying solely on reactive alerts, you will develop and execute hypothesis-driven threat hunting operations across internal telemetry and external sources to uncover fraudulent tools, tactics, and techniques (TTPs) before they impact Stripe’s platform. Central to this work is FT3 (Fraud Taxonomy 3.0), Stripe’s multi-layered taxonomy that decomposes monolithic fraud into structured kill chains.  Collaborating cross-functionally with Fraud Ops, Strategy, Risk, Onboarding, and Security, you will integrate threat intelligence, build agentic simulation workflows, and systematically eliminate product vulnerabilities. Responsibilities • Proactive Threat Hunting & Kill Chain Analysis: Formulate hypotheses and conduct iterative threat hunting operations across Stripe systems and external data. • FT3 Taxonomy: Apply and enrich the FT3 framework across empirical datasets and incidents, standardizing threat intelligence across kill chain phases and targeted API endpoints.   • Threat Intelligence & Signal Expansion: Partner with teams like Fraud Intelligence to integrate, curate, and automate threat feeds into engineering workflows.   • Cross-Functional Advisories & Strategic Controls: Translate raw research and retrospective findings into actionable threat advisories and control recommendations (policy, technical systems, support workflows, and detection mechanisms) for stakeholders across Fraud, Risk, Onboarding, and Security.   • Agentic Testing & Adversary Simulation: Utilize agentic automated testing frameworks to simulate adversary TTPs, validate whether deployed controls interrupt empirical kill chains, and generate regression scenarios to exercise controls.   Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements • 5+ years of experience conducting threat intelligence, threat hunting, or technical incident response within cyber security, product abuse, or trust domains.   • 5+ years of experience analyzing large, complex datasets using data analytics tools to identify anomalies, map behavioral trends, and solve complex fraud problems.   • B.S. or M.S. in Computer Science, Cybersecurity, or a related technical field, or equivalent practical experience.   • Expert proficiency in Python and SQL, with demonstrated experience using code and scripting to automate workflows, build investigative tools, or query big data pipelines.   • Hands-on experience in log analysis (e.g., application logs, API route telemetry, network security events), digital forensics, and cyber investigation methodologies.   • Strong communication skills with a proven ability to translate complex technical research into clear, actionable recommendations and advisories for cross-functional partners. Preferred qualifications • Deep technical understanding of threat actor motivations, infrastructure, and TTPs specific to financial fraud (e.g., ATO, Card Testing, Credential Stuffing).  • Familiarity with standardized taxonomies such as FT3 or MITRE ATT&CK.   • Proficiency with engineering, data processing, and analysis platforms such as Databricks, Trino, PySpark, Pandas, or Scikit-Learn.   • Proven background utilizing Threat Intelligence Platforms (TIPs), tactical threat feeds, OSINT, and breach intelligence.  • Demonstrated capability building or leveraging agentic LLM tools, automated testing systems, or control validation frameworks to model adversary behavior at scale.   • Participation in industry conferences, webinars, or threat-sharing groups, alongside relevant professional certifications (e.g., GCTI, GCFA, OSCP).

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