Member of Technical Staff, Enterprise Evals Platform

Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.

We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.

Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.

Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.

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

• 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.

• 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.

• Professional, academic, or research experience in agent engineering and evaluation, including how agent runtimes and harnesses produce a trajectory and where it fails.

• Bachelor’s Degree in Computer Science or a related technical degree such as Electrical Engineering, Physics, Mathematics, Computer Engineering, Information Systems or Statistics

• 1–5 years in industry level software engineering

• 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.

• 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

Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.