The Fraud Prevention team at Anthropic protects payment and monetization surfaces from financial abuse, building real‑time risk decisioning, dispute and chargeback tooling, and fraud signal engineering at scale. The role focuses on designing systems that balance fraud loss, approval rates, and latency, automating dispute workflows, and developing multi‑layered defenses against evolving fraud tactics. Working cross‑functionally with finance, support, legal, and external payment processors, the engineer will own metrics portfolios, lead investigations into new fraud patterns, and contribute to Anthropic’s broader mission of building safe and beneficial AI.
Staff+ Software Engineer, Financial Fraud en Anthropic
Híbrido - San Francisco, CA; New York City, NY; Seattle, WA
Más vacantes en AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- Proficiency in Python, SQL, and data analysis tools
- Experience building or operating fraud, risk, or abuse detection systems in production
- Strong communication skills and ability to explain complex technical tradeoffs to non-technical stakeholders
- 8+ years of industry software engineering experience, with a focus on payments fraud or risk
- Fluency with payments rails: card networks, payment service providers (Stripe, Adyen), in-app purchase platforms (Apple, Google), refund flows, and the chargeback and dispute lifecycle
- Direct experience combating fraud typologies such as card testing, stolen-card monetization, refund and chargeback abuse, subscription and trial abuse, promotional abuse, and friendly fraud
- Understanding of fraud loss accounting — fraud loss vs. dispute fees vs. card network monitoring programs (VDMP, iVFMP, Mastercard ECP)
- Experience building hybrid rules-and-ML risk systems: real-time scoring at authorization plus post-authorization review workflows
- Experience at a marketplace or subscription business, or on a processor-side or issuer-side risk team
Responsibilities
- Design and build real-time risk decisioning that scores transactions at authorization time, balancing fraud loss, approval rates, and latency constraints
- Build tooling and automation for the dispute and chargeback lifecycle, from review queues to evidence collection and loss reporting
- Engineer fraud signals at scale — device fingerprinting, BIN and issuer signals, velocity features, and cross-account linkage — and detect monetization abuse across subscriptions, trials, promotions, and in-app purchases
- Own a portfolio of metrics — loss rate, dispute rate, authorization approval impact, and false-positive rate — rather than optimizing any single number
- Lead investigations into emerging fraud patterns, building multi-layered defenses designed for attacker adaptation rather than point-in-time rules
- Work cross-functionally with finance, support, legal, and data science, and with external payment processors and platform partners
Technologies
PythonSQLStripeAdyenApple In-App PurchasesGoogle Play BillingDevice fingerprinting toolsBIN and issuer signal processingVelocity feature engineeringCross-account linkage toolsHybrid rules-and-ML risk system frameworks
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