About Mercor

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.

About the Team

The Environments team builds the worlds where frontier AI agents learn to do real work. Each environment recreates a real workplace, with realistic company data, the applications professionals use every day, and expert-designed tasks that test whether an agent can actually get the job done. Frontier labs use these environments to find where their models fall short and to train the next generation to do better.

About the Role

As a Product Manager for Environments, you will own the platform that turns a world into thousands of hard, verifiable training tasks. You will decide what we build to push agents past their current limits, set the quality bar that labs hold us to, and turn work built for one project into products every team can use. What you ship helps shape what goes into frontier training runs, and what the next generation of agents can do.

In this role, you will

Frontier Capability Gaps: Work with researchers at frontier labs to find where their agents fall short, and turn those gaps into clear requirements for the worlds, tasks, and verifiers we build next.

Task Generation at Scale: Build task generation systems that turn a single world into thousands of realistic, hard, verifiable tasks, with controls that let teams steer what gets produced.

Environment Quality Bar: Set the bar for when a world is ready for tasking and when a task is difficult, solvable, and fairly graded, and build the checks that hold every project to it.

Run-by-Run Feedback Loops: Build the feedback loops that show how every run is performing, so teams can find the bottleneck and push yield and difficulty higher with each iteration.

Shared Environment Platform: Turn what works on one project into a platform every team can use, including the tools experts use to review and improve tasks, so each new environment is faster to build than the last.

Roadmap and Success Criteria: Define success in terms of high-quality tasks delivered, balancing throughput, difficulty, and cost, and use lab feedback and agent failures to decide what the platform builds next.

You might thrive in this role if you

Experience: Have 3+ years as a product manager, prior experience in SWE, ML, or DS preferred.

High Ownership: Take full accountability for outcomes, not just outputs.

Independence: Are able to self-direct in ambiguous contexts, creating clarity for others.

Technical Fluency: Are comfortable building scrappy MVPs yourself when needed to validate direction, unblock engineering, or accelerate iteration.