Staff Software Engineer, Fraud & Payment Risk Platform

Role overview This staff-level backend engineering role leads the design and implementation of a platform for fraud prevention and payment risk decisions. The platform brings together live and batch-computed data, configurable business rules, experiments, and machine learning outputs so partner teams can develop and safely operate risk strategies. The work spans architecture, hands-on delivery, production reliability, and cross-team technical leadership.

Responsibilities - Lead architecture and development of decision services, APIs, and shared backend infrastructure. - Build reliable feature ingestion and serving for real-time, near-real-time, and batch data, with attention to freshness and correctness. - Create tools for configuring and versioning risk rules, testing strategies, evaluating experiments, and rolling out changes safely. - Develop and operate engineering workflows across model validation, deployment, inference, versioning, rollback, and production monitoring in partnership with data scientists. - Establish observability and alerting for service health, data quality, rule execution, and model inference; work with partner teams to monitor outcomes. - Lead complex initiatives from design through production, and improve engineering practice through reviews, mentorship, testing, documentation, and safer operations.

Requirements - At least six years of software engineering experience, including substantial production backend work. - At least five years of hands-on Java experience, including service development with Spring Boot. - Experience leading architecture and delivery for distributed systems or shared backend platforms. - Strong understanding of API design, service integration, data modeling, caching, and distributed-system failure modes. - Experience improving performance, scalability, and reliability through measurement, testing, and production troubleshooting. - Experience with cloud infrastructure, containerized deployments, relational and NoSQL databases, and production monitoring. - Ability to guide technical decisions across teams, explain trade-offs, and mentor engineers.

Nice to have Experience with risk or decision platforms, feature systems, configurable rules, experimentation, or production model serving. Familiarity with infrastructure as code, CI/CD, cloud data and container services, and observability or analytics tools is also useful.

Benefits and work setup The listed annual pay ranges by US location tier are $200,000–$250,000, $180,000–$225,000, and $170,000–$212,500. Some specified office locations require two to three days on site each week; candidates elsewhere may be eligible for relocation assistance.