Staff+ Software Engineer, Financial Fraud

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.

Minimum Qualifications

• 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.

Preferred Qualifications

• 8+ years of industry software engineering experience, with a focus on payments fraud or risk.
• Fluency with payments rails: card networks, payment service providers (e.g., 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 (e.g., VDMP, VFMP, Mastercard ECP) — and why chargeback rate thresholds carry existential stakes.
• 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.

Education

• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role.