Fullstack Engineer, Assesment

Mercor•San Francisco, CA

11d•$130,000 - $500,000•Onsite

About The Position

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.

Requirements

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.

Responsibilities

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.