About this role.
Stripe is seeking a senior Abuse Research Engineer to proactively identify and disrupt fraud and product-abuse threats across its financial platform. The role centers on hypothesis-driven threat hunting, adversary kill-chain analysis, threat-intelligence integration, and application of Stripe's FT3 fraud taxonomy. The engineer will use Python, SQL, large-scale telemetry, forensic methods, and agentic testing workflows to validate controls and generate regression scenarios. Close collaboration with Fraud Operations, Risk, Onboarding, Strategy, and Security is required to convert technical research into practical controls and advisories.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
4/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Sample interview questions
I would first define measurable hypotheses around authentication failures, IP and device reuse, credential velocity, endpoint sequences, and downstream account activity. I would query relevant authentication and API telemetry, cluster suspicious behavior, enrich indicators with internal and external intelligence, then map the observed activity to a kill chain. Finally, I would recommend and test layered controls such as rate limits, risk-based challenges, fingerprinting, and detection rules, measuring whether they interrupt the behavior without materially harming legitimate users.
I use SQL to efficiently isolate behavioral patterns in large event datasets and Python to automate enrichment, entity resolution, scoring, and repeatable reporting. For example, I would build a pipeline that identifies anomalous API sequences, joins them to account and payment outcomes, enriches associated infrastructure, and produces prioritized investigation cases. I emphasize versioned logic, clear validation criteria, and monitoring so that successful research can become durable detection or control coverage.
I would begin with the business impact, affected workflow, confidence level, and the attacker path in plain language. I would then provide evidence-backed findings, identify the specific product conditions enabling the behavior, and separate immediate mitigations from longer-term control recommendations. The advisory would assign clear owners, explain expected tradeoffs such as user friction or false positives, and define metrics for validating effectiveness.
A useful simulation reproduces realistic attacker goals, sequences, constraints, and evasive behaviors rather than merely generating synthetic volume. I would define expected control-interruption points across the kill chain, execute safe and authorized test scenarios, and capture telemetry to verify both prevention and detection. The outcome should become a repeatable regression scenario that detects control degradation as products and attacker techniques evolve.
I would use the taxonomy to normalize raw observations into consistent phases, techniques, targets, and indicators so patterns can be compared across incidents and teams. This improves coverage analysis by showing where controls, telemetry, or ownership are missing along the adversary path. I would also enrich the taxonomy with empirical findings so it remains operationally useful for detections, simulations, reporting, and strategic control planning.
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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