Senior Software Engineer designing and scaling risk infrastructure for secure payment flows at Coinbase. Leading initiatives in fraud detection and transaction scoring across global markets.
Responsibilities
Own critical risk outcomes across CDP’s payment platform, including fraud loss, abuse prevention, and user friction — continuously optimizing the trade-off between risk and seamless customer experience.
Develop deep domain expertise in global payment systems and CDP products/APIs (onramps, offramps, transfers, payouts), helping shape a world-class crypto payment risk architecture as these platforms scale worldwide.
Design and build state-of-the-art risk decisioning systems, including core components such as policy/rule engines, machine learning models, real-time scoring services, and autonomous risk evaluation agents.
Architect and implement high-throughput data pipelines (batch, real-time, and streaming) that power risk detection, monitoring, experimentation, and model training at global scale.
Develop advanced detection capabilities spanning classical machine learning, graph-based methods, anomaly detection, and agentic AI systems to proactively identify and mitigate fraud and abuse.
Lead innovation at the intersection of risk, fraud, and agentic AI, conducting cutting-edge research and translating it into production systems.
Contribute to the broader technical community through publications, open research, or engineering blog posts, helping establish Coinbase as a leader in next-generation payment risk technology.
Requirements
Minimum 3 years of software engineering experience
Experience building real-time risk/fraud detection systems at scale
Designing, building, and maintaining production services in a service-oriented architecture
Experience with rules engines and/or ML-based risk scoring (feature engineering, model serving, decisioning)
Working with cross-border payment flows and the risk implications of multi-currency, multi-rail settlement
Leadership skills to develop other engineers and influence risk strategy
Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human‑in‑the‑loop practices to deliver business‑ready outputs and drive measurable improvements in efficiency, cost, and quality.
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