Product Manager, Platform

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.

The Menlo Platform is the software layer that lets developers put agents to work in a humanoid body. Under the hood, it’s a control plane for robots and sessions, real-time video, audio, and skill calls, Python SDKs, a browser-based digital twin, and an Agent Platform that packages, permissions, and deploys agents to Asimov across cloud and on-prem. It’s early, it’s moving fast, and it needs a technical PM to decide what it becomes.

As Product Manager on our Platform team, you’ll own the Menlo Platform end to end, from the first API key a developer creates to an agent running safely on a fleet of Asimovs. You’ll set the roadmap, go deep with engineers on architecture and API design, and talk to users constantly. This is a builder’s PM role. You’ll write technical specs, run the SDK yourself, and spend real time with the robot.

• Own the Menlo Platform end to end, taking it from raw developer and customer signal to shipped features they actually use

• Set and defend the roadmap across the console, SDKs, Digital Asimov, the robot control plane, and the Agent Platform, making clear calls on what we build now, later, and never

• Define the API and SDK surface with engineering, covering resource models, auth and API keys, versioning, and backward compatibility across SDK and protocol releases

• Shape how agents are packaged, permissioned, validated, and deployed to real hardware, including safety envelopes for velocity, workspace, and force, plus rollbacks and fleet observability

• Work through the hard tradeoffs of real-time robotics: latency, connectivity, session lifecycle, and what runs on the robot versus in the cloud

• Define the developer experience from pip install to first deployment, and obsess over time to first robot moving

• Work with Brain and Hardware to expose new capabilities as skills developers and agents can call, and with Deployment to turn enterprise field feedback into platform features

• Talk to users every week, from open-source builders and researchers to enterprise teams running fleets

• Define and instrument platform metrics like activation, session reliability, and deployment success, and use them to decide what’s next

• You’ve shipped developer-facing technical products: platforms, APIs, SDKs, or cloud infrastructure that engineers build on

• You have a real technical foundation, ideally a CS or engineering degree or time as a software engineer, and you can read Python, run a script against an API, and debug your way through a quickstart

• You hold your own in architecture reviews on APIs, auth, real-time streaming, and cloud versus edge

• You write technical specs engineers want to build from, with clear requirements, edge cases, and success criteria

• You make decisions with incomplete information and explain the reasoning well enough that the team commits

• You talk to users by default and can separate what they ask for from what they need

• You use AI as a real collaborator in your day to day work

• Experience in robotics, autonomy, IoT, or other products where software meets physical hardware

• Familiarity with ROS 2, robot simulation, or real-time control systems

• Experience with AI agent frameworks, LLM tooling, or ML platforms

• Hands-on time with WebRTC or real-time media infrastructure such as LiveKit

• Experience with fleet or device management, OTA updates, or on-prem enterprise deployments

• Time building open-source products and working with developer communities

You’ll own the product layer that turns a humanoid into a platform. Every agent a developer builds, every skill Brain ships, and every enterprise deployment runs through what you decide to build. The category is new, the users are early, and the calls you make now will define how people put agents to work in the physical world. If you want to be the PM who figures that out, come build it with us.

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.

We hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.

Originally posted on Himalayas