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Mercor

Product Engineer

San Francisco$130k–$500kfulltimemidAdded today

About this role

Build backend-heavy assessment systems at Mercor that evaluate and match thousands of AI experts to cutting-edge work. You'll design evaluation pipelines, ship full-stack features, and integrate AI-powered scoring at scale in a high-ownership role.

What you'll do

  • Design and build backend services and APIs powering the assessment platform
  • Own features end-to-end from product design through implementation and release
  • Build evaluation pipelines combining human judgment with AI-assisted scoring
  • Contribute across the stack including frontend product surfaces for assessment flows
  • Partner with Product, AI, and Data teams to translate research into production systems
  • Improve performance, reliability, and observability of assessment infrastructure

What they're looking for

  • Backend development (APIs, data modeling, distributed systems)
  • Relational database design and optimization
  • React or modern frontend frameworks
  • LLM integration and AI-powered feature development
  • AI coding tools (Cursor, Claude, GitHub Copilot, or similar)
  • Software testing, debugging, and performance optimization
  • Product intuition and user-centric design
  • Rapid execution in fast-moving environments

Benefits

  • Bi-annual performance bonus
  • Generous equity grant vested over 4 years
  • Up to $15,000 relocation bonus
  • $10,000 proximity bonus (within 0.5 miles of office)
  • $1,500 monthly meals stipend
  • In-person work five days a week in San Francisco, NYC, or London
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Mercor

Mercor builds a marketplace platform connecting expert talent to AI opportunities, supported by identity infrastructure, matching algorithms, and internal tools for data management. The company is hiring Software Engineers, Machine Learning Engineers, Fullstack Engineers, and Security Engineers to develop backend systems, ML models, cloud infrastructure, and distributed platforms.

Website
mercor.io
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Likely interview questions

  • Walk us through a production backend system you've built from scratch—what were the key design decisions around APIs and data modeling?
  • Tell us about your experience building or integrating LLM-powered features. What were the challenges in production?
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