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Mercor

Product Manager, Environments

Mercor is hiring a Product Manager, Environments in San Francisco, United States. It pays $180k-$300k a year and Level rates it ; you can apply on Mercor.

Level's read on this job

  • Works on AI ●●●○ is how Level rates this AI product manager role: the daily work is on AI products, without building the model.
  • The listed pay starts close to the typical AI product manager posting on Level.
  • Tools and skills the posting cites include AI agents.
  • Level lists 86 other open roles at Mercor.
  • The role was posted in the last few days.

AI in this role

ai-agents
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.

  • Strategic Vision: Have a strong ability to connect user needs and business context to long-term product direction.

  • User Obsession and Understanding: Talk to users frequently, know how to effectively conduct user research and customer interviews.

  • Balance of Metrics & Vision: Are equally comfortable running rigorous experiments and pushing forward visionary bets.

Benefits

  • Bi-annual performance bonus structure

  • Generous equity grant vested over 4 years

  • Up to $15k Relocation bonus

  • $10K housing bonus (if you live within 0.5 miles of our office)

  • $1.5K monthly stipend for meals

  • 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.

How we rate this

Product Manager, Environments at Mercor rates 73 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agents

Also common in AI product manager jobs at this level

Pay

This job starts at
$180k
Typical AI product manager job
$178k

This posting starts close to the typical AI product manager job. See AI product manager jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Describe a typical day in a role like this one: which parts run through AI directly?
  3. If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

  • List these exact terms on your resume: AI agents. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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