Member of Technical Staff (Software Engineer, Fraud Prevention)

Perplexity; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Perplexity is seeking a software engineer to own backend services for fraud prevention. You will design, build, and operate high-throughput systems, collaborate with ML engineers, fraud analysts, and product teams, and ensure low-latency, reliable production deployments across sign-up, payments, and API usage.

You will work with Rust/Go/C++, Kubernetes, and Terraform, focusing on real-time data pipelines, feature computation, and seamless integration into product workflows.

Qualifications

• 5+ years building and operating high-throughput, latency-sensitive backend systems in production.
• Experience with Rust, Go, or C++ (Rust preferred).
• Hands-on experience with Kubernetes, Terraform and cloud infrastructure.
• Solid data fundamentals: stream/batch computations, caching, and storage trade-offs.

Responsibilities

• Design, build, and operate high-throughput, low-latency backend services for fraud detection and prevention.
• Build data pipelines and real-time feature computation powering detections and ML models.
• Collect and process trust signals.
• Integrate fraud decisions into product, auth, and payments flows across the company.
• 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.

Skills

Latency-sensitive systems

Ownership mindset

Strong debugging

Tools

Kubernetes

Terraform

Cloud platforms (AWS/GCP)

Job description

About the role

the company'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 the company
• 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