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Mercor

Fullstack Engineer, Assesment

San Francisco$130k–$500kfulltimemidAdded today

About this role

Mercor is seeking a Fullstack Engineer to design and build backend-heavy systems for their AI-powered talent assessment platform. You'll own features end-to-end—from evaluation pipelines and APIs to candidate-facing product surfaces—working directly with product and AI teams to scale assessment infrastructure that impacts tens of thousands of experts.

What you'll do

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

What they're looking for

  • Backend development and API design
  • Relational databases and data modeling
  • Distributed systems architecture
  • React/TypeScript and modern frontend frameworks
  • LLM integration and AI-powered feature development
  • Testing, debugging, and performance optimization
  • AI development tools (Cursor, Claude Code, GitHub Copilot)
  • Product intuition and user-centric thinking

Benefits

  • Bi-annual performance bonus structure
  • Generous equity grant vested over 4 years
  • Up to $15,000 relocation bonus
  • $10,000 proximity bonus for living within 0.5 miles of office
  • $1,500 monthly meals stipend
  • In-person work in San Francisco, NYC, or London offices
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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 backend service you designed and deployed—how did you approach scalability and reliability?
  • Describe your experience building or integrating LLM-powered features. What challenges did you face?
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