Member of Technical Staff, Enterprise Evals Platform

Mercor; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Relocation bonus

Housing stipend

Meals stipend

Equity grant

Free Equinox membership

Laundry reimbursement

Wellness reimbursement

Health insurance

Job summary

Mercor in San Francisco is seeking an experienced Platform Engineer for our Enterprise Agent Eval Systems. You will design verifiers, build offline environments, and craft scalable grading infrastructure that works across customers, domains, and tasks.

You will collaborate with the Enterprise Platform and Applied AI teams to codify evaluation practices, reduce system friction, and enable reliable, measurable improvements in agent performance at scale.

Qualifications

• Experience in building evaluation systems for agent or LLM runtimes and benchmarks.
• Ability to turn qualitative quality notions into measurable rubrics for model improvements.
• Strong software engineering fundamentals and independent problem solving.

Responsibilities

• Define golden sets by decomposing real tasks and encoding expert quality bars.
• Build verifiers over agent trajectories and outputs, calibrated and hard to game.
• Build the evaluation platform that runs offline environments and grading at scale.
• Analyze production trajectories and turn failure modes into regression tests.
• Run the optimization loop across models, prompts, skills, and harnesses.
• Own rollout gates that decide whether an agent change ships.
• Collaborate with the Enterprise Platform team and Applied AI engineers embedded with customers.

Skills

Agent engineering

Evaluation suites

LLM/agent systems

Judgment design

Tools

Harbor environments

RL environments

Job description

About Mercor

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.

About the Role

Enterprise agents are complex systems, and they only pay off when their work is reliable and economically viable. Evaluation is how you get both: checking correctness is the obvious case, and routing is the subtler one, since choosing a model against cost, latency, and quality requires quality to be measurable at all. Knowing where the bar sits is the hard part. You decompose real work, take the standard from the practitioners who hold it, and encode it so an agent cannot shortcut it. You will apply what Mercor has learned building benchmarks with domain experts, and devise new methods, so that evals and rubrics keep improving and so do the agents measured against them. That work only scales with a platform behind it. This is a platform engineering role with good depth of understanding in evals. You will build the verifiers, the environments agents are measured in, and the grading infrastructure that runs at scale, abstracted across customers, domains, and tasks so that every run becomes evidence the next agent inherits instead of starting over. Read more about how we think about this: Agent Eval Systems

Responsibilities

• Define golden sets: decompose real tasks and encode the expert quality bar.
• Build verifiers over agent trajectories and outputs, calibrated and hard to game.
• Build the eval platform that runs offline environments, task suites, and grading at scale.
• Run loss analysis over production trajectories and turn failure modes into regression tests.
• Run the optimization loop across models, prompts, skills, and harnesses.
• Own the rollout gates that decide whether an agent change ships.
• Partner with the Enterprise Platform team and the Applied AI engineers embedded with customers.

What We're Looking For

• Professional, academic, or research experience in agent engineering and evaluation, including how agent runtimes and harnesses produce a trajectory and where it fails.
• Experience building evaluation suites for LLM or agent systems, and familiarity with how benchmarks such as terminal-bench, tau-bench, and APEX are constructed and where they get gamed.
• Judgment about task and rubric design: turning a fuzzy notion of quality into something measurable, with agent or model improvements to show for it.
• Strong software engineering fundamentals, and the ability to work independently on ambiguous, loosely specified problems.
• Bonus: experience with Harbor environments and RL environments.

Why Mercor

• Impact: No agent reaches an enterprise customer without clearing the bar you set.
• Learning: Eval work spanning frontier model measurement and live enterprise deployments, on production trajectories few teams get to see.
• Growth: Research and systems in one role, with fast paths to owning the eval system end to end.

Benefits

• Up to $15k relocation bonus
• $10K housing bonus (if you live within 0.5 miles of our office)
• $1.5K monthly stipend for meals
• Generous equity grant vested over 4 years
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement
• Health, Dental, Vision insurance