Staff Security Engineer, Abuse Control

What you’ll do

As an Abuse Control Engineer on the ACE team, you will design, prototype, and incubate technical defenses that safeguard Stripe’s financial ecosystem against complex, cross-cutting abuse vectors.

When emerging threat patterns reveal weaknesses in Stripe products, ACE rapidly builds and experiments with technical safeguards. Guided by empirical evidence and Stripe’s Fraud Taxonomy 3.0 (FT3) framework, you will translate threat intelligence into precise technical control requirements, such as API rate limits, step-up challenges, parameter validation, and pre-debit holds.

You will run experiments to evaluate risk reduction against the impact on legitimate user conversion, carefully balancing security and product velocity. You will manage controls through a structured incubation lifecycle, build automated regression suites to prevent threats from recurring, and partner with product engineering teams to transfer mature, long-term defenses.

Responsibilities

• Rapid control prototyping: Design, prototype, and deploy technical controls across API, protocol, and product boundaries to address high-impact abuse vectors.
• Evidence-based technical requirements: Translate empirical attacker evidence and FT3 threat research from Abuse Research, Fraud, and Security teams into precise technical abuse requirements and control specifications.
• Control co-design: Collaborate with teams across Stripe to design resilient, secure controls across payments, onboarding, identity, and Connect surfaces.
• Risk experimentation: Run rigorous experiments and A/B tests to measure risk reduction against the impact on legitimate user conversion, optimizing controls to minimize friction while neutralizing threats.
• Regression testing: Build comprehensive regression test suites and automated attack simulations with Abuse Research to ensure mitigated abuse vectors do not recur.
• Stakeholder management: Execute ACE’s incubation model by defining handoff criteria, operational documentation, timelines, and target dates for transferring successful controls to product teams.

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

• 10+ years of experience in security engineering, software engineering, application security, or anti-abuse engineering in a high-scale production environment.
• A bachelor’s or master’s degree in computer science, cybersecurity, software engineering, or a related technical field, or equivalent practical experience.
• A strong software development background, with proficiency in Python, Go, Java, or a similar production programming language, as well as advanced SQL skills for analyzing system telemetry.
• Experience using frontier AI models for software development, learning, and analysis.
• Hands-on experience building API-level safeguards, rate-limiting frameworks, authentication or authorization checks, or input-validation controls.
• Experience with automated testing frameworks, including writing unit, integration, and regression tests for critical backend software.
• Strong cross-functional collaboration and communication skills, with a track record of partnering across security, product, and platform teams to drive technical outcomes.

Preferred qualifications

• A track record of designing and executing A/B tests, evaluating control efficacy, and balancing security safeguards against user conversion friction.
• Expertise in threat modeling, secure systems architecture, and modern application-security design principles.
• Familiarity with established threat frameworks, such as FT3 or MITRE ATT&CK, and experience applying adversary kill-chain analysis to build resilient defenses.
• Knowledge of financial fraud vectors, threat-actor tactics, techniques, and procedures, and attacker infrastructure, including account takeover, card testing, and credential stuffing.
• Hands-on experience with large-scale data-processing platforms, such as Databricks, Trino, or PySpark, to monitor and measure control performance across distributed systems.
• Experience incubating software features, establishing clear operational handoff criteria, and transitioning ownership to partner engineering teams.