Product Manager, Agents & Auto-Research

Join the team bringing autonomy to the built world

At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver more of the critical infrastructure our society needs.

In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks.

About the Role

Most agents are judged on benchmarks. Ours are judged on whether a 40-ton machine works better next week.

Bedrock is building a company that scales with data, compute and agents, so that each new capability, machine and decision costs less and lands faster than the last. Agents already do real work here, at scale: they triage field issues, assemble release evidence, draft plans and write most of our code. And we're already seeing how autonomous research can unlock new capabilities by leveraging the right data, evaluation and sim.

You'll own the product for this, end to end:

• The agent platform. Where agents at Bedrock run, get evaluated, earn trust for load-bearing work, and get measured.
• Robotics auto-research. Agent loops that improve our autonomy stack, our data, our simulation and our infrastructure, faster than a team of people could on its own.

You'll report to the Head of Product and work daily with our agent platform, ML, simulation and autonomy engineers.

What You'll Do

• Set the strategy and roadmap for agents and auto-research at Bedrock. Decide which loops to build next based on how much they move capability, cost and speed.
• Define what it takes for an agent to be load-bearing: the evals, reliability bar, human oversight and feedback loop a team needs before it relies on one.
• Turn auto-research from a few promising loops into a production system, with budgets, verdicts, provenance and a clear line from experiment to a capability that ships.
• Own the metrics that show whether this works, such as agent adoption, the share of capability work done by auto-research, and cost and time per result. Get them measured, then get them goaled.
• Make our internal systems agent-ready so agents can read, act and be held accountable across data, simulation, code, planning and decisions.
• Keep us model-agnostic as the frontier moves: the best model for each workload, while context, traces and memory stay ours.
• Build. Prototype agents, evals and workflows yourself with AI coding tools instead of writing specs about them.
• Help drive adoption across engineering, operations and commercial teams, and be the person who knows when an agent should not be trusted.

What We're Looking For

Required

• 5+ years in product, research or engineering roles, including shipping LLM agents or AI systems that people rely on in production.
• Hands-on depth in how frontier models and agents behave: harnesses, tool use, context, evals, cost and failure modes. You form your own view of a new model within days of its release.
• Experience designing evaluations for open-ended model behavior and making product calls from them.
• AI-native in your own work. You use Claude Code, Codex or similar every day, read code fluently and ship prototypes.
• Comfort with ML experimentation and research workflows: you can follow a research discussion with engineers and push on its rigor.
• Clear, concise writing. You turn an ambiguous direction into a plan with measurable outcomes that a team can act on.
• A track record of agency: you've taken a problem nobody owned and carried it to a real result.

Preferred

• Experience at a frontier AI lab or AI-native startup working on agents, evals, RL environments or automated research.
• Background in robotics, autonomy, simulation or ML infrastructure.
• Built an internal developer or research platform adopted across many teams.
• Published research or open-source work on agents, evals or AI for science.

Why This Role

• A real-world test bed. Experiments close against physical ground truth from a live fleet, not a leaderboard.
• Agents as the operating model, not a side project. Our tech scaling plan assumes agents and auto-research do a growing share of the work, and leadership measures it.
• Scope and speed. You'll own a frontier problem at a company small enough that what you ship changes how everyone works within weeks.