Staff+ Software Engineer, Financial Fraud

About the role

The role centers on designing and maintaining systems that directly prevent financial abuse across Anthropic's monetization stack. You will own the real-time risk decisioning layer that scores transactions at authorization, balancing fraud loss against approval rates and strict latency requirements. A core responsibility is building the tooling and automation that manages the full dispute and chargeback lifecycle, from triage queues to evidence assembly and loss reporting. You will engineer scalable fraud signals, including device fingerprinting, BIN and issuer data, velocity features, and cross-account linkage to detect subscription, trial, promotional, and in-app purchase abuse. The position requires seeing from attackers' perspectives, anticipating how they might respond to new countermeasures, and ensuring that false positives are minimized to protect legitimate paying customers. You will work cross-functionally with finance, support, legal, and data science teams while also collaborating with external payment processors and platform partners to align on fraud strategies.

Key facts

What you'll do

• 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.
• Implement detection logic for card testing, stolen-card monetization, refund and chargeback abuse, subscription and trial abuse, promotional abuse, and friendly fraud.
• Maintain and iterate on rules-based and machine-learning risk systems to operate reliably in production at scale.
• Define and track key risk indicators and ensure that changes to risk strategies do not materially degrade user experience or authorization performance.
• Participate in on-call rotations to respond to high-severity fraud incidents and support rapid mitigation and resolution.

Requirements

• Proficiency in Python, SQL, and data analysis tools is required for manipulating large datasets and producing investigative insights.
• Experience building or operating fraud, risk, or abuse detection systems in production is mandatory to understand operational realities and failure modes.
• Strong communication skills are essential to explain complex technical tradeoffs to non-technical stakeholders and to align on action plans.
• Fluency with payments rails such as card networks, payment service providers like Stripe and Adyen, in-app purchase platforms including Apple and Google, refund flows, and the chargeback and dispute lifecycle is required.
• 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 is mandatory.
• Understanding of fraud loss accounting, including fraud loss versus dispute fees versus card network monitoring programs like VDMP and i VFMP, and why chargeback rate thresholds carry existential stakes for the business.
• Experience building hybrid rules-and-ML risk systems that combine real-time scoring at authorization with post-authorization review workflows is required.
• Prior experience at a marketplace or subscription business, or on a processor-side or issuer-side risk team, is mandatory to bring relevant context and proven patterns.

Nice to have

Only items explicitly noted as preferred qualifications in the source are listed here, and no additional preferences are introduced.

Practical notes

This is a full-time position. The compensation range provided reflects an annual salary framework. Relevant experience with payments fraud, risk systems, and abuse detection is explicitly required, and prior background in marketplace or subscription risk or on processor-side teams is mandatory. No specific visa, travel, or deadline information is provided in the source.