Product Manager, Agent Harness (Robotics)

Menlo • Palo Alto, CA

11d

About The Position

Menlo Research is an applied R&D lab building Asimov, an open-source humanoid robot platform and its full software stack. The Product Manager for the Agent Harness will own the evolution of Menlo’s robotics agent platform, which researches engineering solutions, helps design manufacturable robots, trains them in simulation to perform physical tasks, and supports real-world deployment.

This is a deeply technical product role for someone with experience building coding agents and the ability to apply that experience to robotics and physical AI. The Product Manager will work directly with technical users, researchers, and engineers; prototype workflows; and turn an ambitious product vision into focused releases that ship. The role spans agent behavior and reliability, user workflows, robotics applications, and collaboration with specialists across Brain, Hardware, and Platform.

Requirements

• Experience building a coding agent such as Codex, Claude Code, Cursor, Devin, Lovable, or a comparable product, as a product manager, engineer, or technical founder, with meaningful ownership of shipped capabilities.
• Deep understanding of agent systems and ability to explain tradeoffs involving planning, tool use, context and state management, execution environments, verification, and recovery during long-running tasks.
• Hands-on ability to navigate unfamiliar codebases, prototype agent workflows, integrate tools, and investigate failures through code and execution traces.
• Experience improving agent reliability measurably using evaluations, user feedback, and telemetry to improve task completion, reduce human intervention, and manage latency and compute.
• Strong product judgment for autonomous workflows, including how users delegate complex work, inspect results, intervene, and recover from errors.
• Ability to learn unfamiliar technical domains through papers, code, experiments, and collaboration with specialists, and to build robotics knowledge for informed product decisions.
• Ability to own outcomes, work directly with technical users, and turn ambitious ideas into focused releases with measurable acceptance criteria.

Nice To Haves

• Familiarity with robotics and physical AI, such as inverse kinematics, motion planning, control, reinforcement learning, imitation learning, vision-language-action models (VLAs), or world-action models (WAMs).
• Experience with room scanning, 3D reconstruction, scene understanding, or converting real environments into simulation assets.
• Experience with simulation and robot learning infrastructure, including training pipelines, reproducible experiments, skill composition, and validation on physical robots.
• Familiarity with robot design and manufacturability, including CAD, component selection, bills of materials, assembly, sourcing, and cost constraints.

Responsibilities

• Own the agent harness end to end, translating the product vision into focused releases with measurable acceptance criteria across research and engineering.
• Define how the agent plans, uses tools, manages context and state, executes and verifies work, and recovers during long-running engineering and robotics tasks.
• Shape how users delegate complex physical-world work to the agent, inspect results, intervene, and recover from errors; translate those needs into clear product behavior and a strong developer experience.
• Work directly with technical users to identify initial high-value use cases across engineering research, robot design, simulation training, and deployment.
• Improve agent reliability using evaluations, user feedback, and telemetry; increase task completion, reduce human intervention, and manage latency and compute.
• Prototype agent workflows, integrate tools, and investigate failures through code and execution traces.
• Build robotics knowledge, including VLAs, world-action models, simulation, and manufacturability, while working with Brain, Hardware, and Platform specialists to make informed product decisions.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed