Product Engineer, Talent Experience

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

As a Member of Technical Staff on the Talent Experience team, you'll build the systems that evaluate the skills and expertise of the world's top talent at scale.

Assessment is at the core of how Mercor works: every expert in our network is vetted, matched, and continuously evaluated through our assessment platform. You'll own backend-heavy, full-stack work across this platform—designing evaluation pipelines, building the services that power AI-assisted interviews and skill verification, and shipping the product surfaces that tens of thousands of candidates and experts interact with every day.

You'll work directly with the Assessment product lead on roadmap and execution, with significant ownership from day one. This is a high-leverage role: improvements to assessment quality and throughput compound across everything Mercor does.

• Design, build, and ship backend services and APIs that power Mercor's assessment platform at scale.

• Own features end-to-end—from product discussions and technical design to implementation, testing, and release.

• Build evaluation pipelines and data models that combine human judgment with AI-powered scoring.

• Contribute across the stack, including the candidate- and expert-facing product surfaces of assessment flows.

• Partner closely with Product, AI, and Data teams to translate assessment research into production systems.

• Leverage modern AI coding tools to accelerate development while maintaining a high engineering bar.

• Improve the performance, reliability, and observability of assessment infrastructure.

• Solve real customer problems and continuously improve candidate and expert experience.

• 3+ years of professional software engineering experience building production systems.

• Strong backend fundamentals: API design, data modeling, distributed systems, and relational databases.

• Comfortable working across the stack, including modern frontend frameworks (e.g. React/TypeScript) when the product needs it.

• Experience building or integrating LLM-powered features in production is a strong plus.

• Comfortable building products using modern AI development tools (e.g. Cursor, Claude Code, GitHub Copilot, Codex, or similar) to accelerate software development.

• Strong software engineering fundamentals, including testing, debugging, and performance optimization.

• Excellent product intuition—you enjoy building products that people actually use.

• Comfortable working in fast-moving environments with evolving priorities.

• High ownership, curiosity, and a bias toward shipping.

• Impact: Build the evaluation systems that decide how tens of thousands of AI experts are matched to the world's most important AI work.

• Ownership: Join a small, high-velocity engineering team where you'll own features end-to-end and shape the assessment roadmap from day one.

• Learning: Work alongside world-class engineers, product leaders, and AI researchers building products at the frontier of AI.

• Growth: Rapid career progression with opportunities to expand ownership as the assessment platform and engineering organization scale.