Member of Technical Staff (Software Engineer, Fraud Prevention)

About the role

Perplexity's fraud prevention team protects our products, users, and infrastructure from fraud and abuse across the whole lifecycle: sign-up, authentication, payments, API usage, and product features. We build and operate a real-time decisioning platform together with the surrounding systems: data pipelines for detection and model training, integrations with product and payment systems, and internal tooling for investigation and response. As a software engineer on the team, you will own services end to end, from design to production operations, and work closely with fraud analysts, ML engineers, and product teams to make abuse expensive and legitimate usage frictionless.

Responsibilities

Design, build, and operate high-throughput, low-latency backend services for fraud detection and prevention

Build data pipelines and real-time feature computation that power detections and ML models

Collect and process trust signals

Integrate fraud decisions into product, auth, and payment flows across Perplexity

Own reliability of production services: observability, alerting, safe rollouts, incident response

Build internal tooling for investigation, enforcement, and auditability

Partner with fraud analysts to turn new abuse patterns into production detections quickly

Qualifications

5+ years building and operating high-throughput, latency-sensitive backend systems in production

Strong systems programming skills; Rust, Go, or C++ experience preferred (we write Rust)

Hands-on experience with Kubernetes, Terraform and cloud infrastructure

Solid data fundamentals: stream/batch computations, caching and storage trade-offs

Pragmatic, ownership-driven approach: ship incrementally, measure, and make failures degrade safely

Nice to have

Fraud prevention, anti-abuse, or payments-risk domain experience

Experience serving ML models in production

Client-side experience (web or mobile) for signal collection

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