Menlo - Product Manager, Agent Harness (Robotics)
Requirements
• 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, taking it from an ambitious vision to focused releases with measurable acceptance criteria across research and engineering • Define how the agent plans, uses tools, manages context and state, executes, verifies its 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, and turn those needs into clear product behavior and a delightful developer experience • Work directly with technical users to find the initial use cases where the harness creates real value, across engineering research, robot design, simulation training, and deployment • Drive agent reliability through evaluations, user feedback, and telemetry, raising task completion, reducing human intervention, and managing latency and compute • Prototype agent workflows, integrate tools, and investigate failures yourself through code and execution traces • Get fluent in robotics fast, from VLAs and world-action models to simulation and manufacturability, working alongside our Brain, Hardware, and Platform specialists to make informed product calls • What We Look For • What We Look For • You’ve built coding agents, having worked on Codex, Claude Code, Cursor, Devin, Lovable, or a comparable coding agent as a PM, engineer, or technical founder, with meaningful ownership of shipped capabilities • You understand agent systems deeply and can explain the design decisions and tradeoffs behind planning, tool use, context and state management, execution environments, verification, and recovery during long-running tasks • You’re hands-on: you navigate unfamiliar codebases, prototype agent workflows, integrate tools, and independently investigate failures through code and execution traces • You’ve made agents measurably more reliable, using evaluations, user feedback, and telemetry to improve task completion, reduce human intervention, and manage latency and compute • You have strong product judgment for autonomous workflows, understanding how users delegate complex work, inspect results, intervene, and recover from errors • You learn unfamiliar technical domains fast through papers, code, experiments, and collaboration with specialists, and you’re ready to build the robotics knowledge needed to make informed product decisions • You own outcomes, working directly with technical users and turning ambitious ideas into focused releases with measurable acceptance criteria
Benefits
• You’ll define what a coding agent becomes when its output is a working robot. The harness touches every part of how Asimov gets built: the research, the design, the training, and the deployment. The category doesn’t exist yet, the team ships to real hardware every week, and we build in the open. If you’ve helped build the agents that changed software and want to do it again for the physical world, come build it with us. • A Note on AI • You don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on. • Equal Opportunity and Accommodations
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