Real-Time Fraud Backend/API Engineer

Stripe, Inc.; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Stripe Radar is a fraud prevention platform that helps businesses understand and stop fraud and abuse across Stripe’s global payments network. As a software engineer on Radar, you will build backend services, enterprise APIs, and user interfaces that empower customers to investigate threats and configure defenses.

You will own problems from investigation through launch, collaborating with product managers, designers, and data scientists to scale systems and bring ML/AI capabilities into

Qualifications

• 2+ years of industry software engineering experience.
• Strong coding skills in any programming language with production software experience.
• Ability to work across systems and learn unfamiliar codebases.
• Rigorous data-driven approach to problem solving.
• Strong collaboration and communication across disciplines.
• Ownership mindset and initiative in ambiguous problems.

Responsibilities

• Build and operate reliable, low-latency services that detect fraud at scale.
• Design APIs and dashboards for threat investigation and defense configuration.
• Develop shared platform infrastructure enabling Radar capabilities.
• Work with users to understand emerging threats and translate into products.
• Partner with ML engineers to bring signals and AI into production.
• Own projects end-to-end from design to rollout and iteration.
• Maintain high engineering quality and reliability while threats evolve.

Skills

Software engineering

Production software

Cross-disciplinary collaboration

Data-driven problem solving

Ownership mindset

Ambiguity navigation

Job description

Stripe Radar is a fraud prevention platform that helps businesses understand and stop fraud and abuse across Stripe’s global payments network. As a software engineer on Radar, you will build backend services, enterprise APIs, and user interfaces that empower customers to investigate threats and configure defenses.

You will own problems from investigation through launch, collaborating with product managers, designers, and data scientists to scale systems and bring ML/AI capabilities into