The posting
About Menlo
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 Role
Asimov is leaving the lab. Teams are buying fleets, commissioning custom builds, and wiring their own agents into a humanoid body. Every one of those deployments needs someone who can make it work on site. That’s you.
As a Forward Deployed Engineer on our Deployment team, you’ll embed with Menlo’s enterprise customers and own their Asimov deployments end to end, from a scoped use case to a robot doing real work in their environment. You’ll write the integration code, configure the platform, debug the robot on the floor, and bring back what you learn so the next deployment goes faster than the last. You’ll partner with our Enterprise Lead on the customer side, and with Brain, Platform, and Hardware on everything behind it.
What You’ll Do
- Own customer deployments end to end, taking each one from a scoped use case to Asimov running reliably in the customer’s environment
- Build integrations that connect customer agents, software, and workflows to Asimov through the Menlo Platform’s Python SDK, control plane APIs, and Agent Platform
- Package customer agents for deployment and configure their permissions and safety envelopes, including velocity limits, workspace boundaries, and force constraints, for each site
- Prototype and validate tasks in Digital Asimov and simulation before they touch hardware, then bring them up on real robots on site
- Stand up deployments on Menlo Cloud or in customer on-prem environments, working through networking, auth, and real-time connectivity on the ground
- Diagnose problems across the full stack, from actuators and firmware to perception, policies, and agent behavior, using telemetry and logs to fix them or hand them off with a clean repro
- Be the customer’s technical point of contact: run onboarding, train their engineers, and set honest expectations on what the robot can and can’t do today
- Turn field learnings into product by feeding bugs, missing capabilities, and deployment data back to Platform and Brain, and write the playbooks and tooling that make every future deployment faster
- Travel to customer sites whenever a deployment needs hands on the robot
What We Look For
- You’ve worked in a forward deployed, solutions, or field engineering role, ideally on physical systems like robotics, autonomy, or industrial automation
- You write production-quality Python, live comfortably in Linux, and can read C++ when that’s where the bug lives
- You know your way around ROS 2, Docker, and networking, because robots on customer Wi-Fi are their own adventure
- You’ve debugged systems in the field where the problem could be software, hardware, network, or the environment itself, and you found it
- You can explain a policy failure to a customer’s operations lead and walk our engineers through a stack trace in the same afternoon
- You take ownership: you’d rather unblock the customer tonight and ship the clean fix tomorrow than wait for perfect
- You use AI as a real collaborator in your day to day work
Nice to Have
- Experience connecting LLM agents or agent frameworks to physical systems
- Hands-on time with humanoids, legged robots, or manipulators
- Familiarity with simulation tools like Isaac Sim or MuJoCo, and the realities of sim-to-real
- Comfort with embedded and hardware debugging: motor drivers, CAN, sensors, power
- Experience with WebRTC or real-time media infrastructure such as LiveKit
- Experience with enterprise on-prem deployments, security reviews, or IT integration
- Time as one of the first customer-facing engineers at an early-stage startup
Why Join Menlo
You’ll sit where every Menlo system meets the real world: the agent platform, simulation, policy training, the Asimov hardware, and the data engine. Deployments are where the robot gets tested for real, and what you learn on site directly shapes what we build next. Small team, real robots, open source by default, and customers who are betting on us early. If you want to be the person who makes humanoid labor work outside the lab, 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
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.
