Member of Technical Staff, Agentic Systems

Mercor•San Francisco, CA

6d•$130,000 - $500,000•Onsite

About The Position

Mercor is 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 Talent Experience, you'll build the backend systems and agent infrastructure behind every journey in Mercor's talent network. A growing share of Mercor's core work runs on agents: systems that source, screen, match, and support experts with far less human intervention than the workflows they replace. You'll design and ship the services those agents run on—orchestration, tool interfaces, evaluation and guardrail layers, and the data models that keep agent decisions auditable and reversible. This is production agent engineering at real volume, against a network of tens of thousands of experts and the demands of the world's leading AI labs. This is a hands-on engineering role. You'll write code, own systems end to end, and work directly with product, operations, and AI researchers to decide what agents should do and where humans still belong in the loop.

Requirements

5+ years of professional backend engineering experience.
Strong fundamentals in distributed systems, service architecture, data modeling, and API design at scale.
Hands-on experience building with LLMs in production—agents, tool use, retrieval, or evaluation systems—or a clear track record of learning new systems fast and shipping them.
Comfort with ambiguity: agent systems fail in ways traditional services don't, and the right design is rarely obvious upfront.
A track record of owning systems end to end, from design through production operation.
Fluent in using modern AI development tools (e.g. Cursor, Claude Code, GitHub Copilot, or similar) to improve engineering productivity.
Strong opinions, loosely held: you drive alignment through technical judgment and clear writing, not authority.
Excellent communication skills—able to make complex tradeoffs legible to both engineers and leadership.
High ownership, pragmatism, and a bias toward shipping.

Responsibilities

Design, build, and own the backend services powering Mercor's agentic workflows—orchestration, tool calling, retries and fallbacks, and human-in-the-loop review.
Ship agents into production and iterate on them against real usage: prompts, tools, guardrails, and cost and latency tradeoffs.
Build the evaluation infrastructure that tells us whether an agent is actually working—offline evals, online metrics, and regression detection.
Design core services, data models, and APIs that scale with the talent network and stay legible as agent behavior changes underneath them.
Make agent decisions observable, auditable, and reversible—so failures are caught early and understood quickly.
Partner with product, operations, and AI researchers to decide what should be an agent, what should stay deterministic, and where a human belongs in the loop.
Establish backend engineering standards and patterns, and raise the technical bar of the engineers around you.