This role pays less than
58%
of similar roles. Most pay
$162,000–$234,150
— the shaded band above.
At the midpoint, this role pays about
$180k
versus about
$198k
for comparable roles.
Based on 240 similar postings.
Employer
About Stripe
Stripe is a financial infrastructure platform for internet businesses, providing payment processing, billing, fraud prevention, and banking-as-a-service APIs to businesses of all sizes globally. Industry: Payments Infrastructure & Financial Technology
Stripe currently has
99 open roles
on FindRole.
Listed pay typically runs
$203,600–$288,000
across 96 roles with salary data.
As an Abuse Research Engineer within the Abuse Research Group, you will proactively hunt for advanced threats and dissect complex fraud vectors to safeguard the financial ecosystem. You will perform hypothesis-driven threat hunting across internal telemetry and external sources to uncover fraudulent tools, tactics, and techniques before they impact the platform. Your daily work involves applying the FT3 framework to decompose fraud into structured kill chains, building agentic simulation workflows, and translating research into actionable technical advisories for cross-functional partners. You will utilize Python, SQL, and data analytics tools to query big data pipelines and automate investigative workflows. The role requires expertise in log analysis, digital forensics, and cyber investigation methodologies to identify anomalies and map behavioral trends while utilizing automated testing frameworks to simulate adversary behavior and validate security controls across various API endpoints.
Conduct hypothesis-driven threat hunting operations across internal telemetry and external data sources to uncover fraud tools and tactics.
Apply the FT3 framework to decompose complex fraud into structured kill chains and standardized intelligence.
Integrate, curate, and automate threat feeds from various sources into engineering workflows.
Translate research findings into actionable threat advisories and technical control recommendations for cross-functional stakeholders.
Build agentic automated testing frameworks to simulate adversary behaviors and validate security controls.
Develop regression scenarios to systematically identify and eliminate product vulnerabilities.
Analyze large, complex datasets using Python and SQL to identify anomalies and map behavioral trends.
What we're looking for
5+ years of experience in 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 and map behavioral trends.
B.S. or M.S. in Computer Science, Cybersecurity, or a related technical field, or equivalent practical experience.
Expert proficiency in Python and SQL for automating workflows, building investigative tools, and querying big data pipelines.
Hands-on experience in log analysis, digital forensics, and cyber investigation methodologies.
Strong communication skills to translate complex technical research into actionable recommendations for cross-functional partners.
Deep understanding of threat actor motivations and TTPs specific to financial fraud (preferred).
Familiarity with FT3 or MITRE ATT&CK, proficiency in data platforms like Databricks/PySpark, and experience with TIPs or agentic LLM tools (preferred).