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
We are looking for a Technical Program Manager to own how robot data gets used within our research teams. You will be the connective tissue between researchers who define what data we need, the engineers who build the rigs and pipelines, and the operators who capture it in the real world. This is a hands-on role. Expect to debug a teleop rig one hour and design a data ingestion workflow the next.
What You'll Do
Own the end-to-end lifecycle of robotics data collection programs, from research requirements to delivered datasets.
Translate research goals into concrete collection protocols, task designs, and quality bars.
Run collection operations across multiple sites and teleoperators, keeping throughput, quality, and cost on track.
Partner with engineering to build and improve pipelines for high-fidelity sensor data such as video, robot logs, and teleop trajectories.
Set up and maintain physical collection rigs and hardware, and scale them as demand grows.
Define and track KPIs like throughput, yield, cost per hour of data, and turnaround time.
Find the bottlenecks, then build the fix. Do not just flag problems.
Keep researchers, engineers, and operators aligned on priorities and timelines.
What We Look For
Track record running technical programs with real operational complexity.
Comfort in the weeds of both hardware and data. You can reason about a sensor rig and a data pipeline.
Has personally run hands-on data collection before, not just managed it from a distance.
Strong grasp of data QA across the full path from collection to training, and can diagnose why data is failing and iterate it from unusable to training-ready.
A data-driven mindset. You measure what you run and improve it.
Strong systems thinking and process design. You build workflows that hold up at scale.
High operational rigor and a bias for action in ambiguous, fast-moving conditions.
Clear communication across research, engineering, and field teams.
Nice to Have
SQL or Python, enough to pull your own data and build your own dashboards.
Experience with robotics, teleoperation, autonomous vehicles, or large-scale data collection.
Familiarity with sensor calibration and the realities of capturing physical world data.
Exposure to VLA models, computer vision, or ML training data requirements.
Experience coordinating distributed teams or collection sites across time zones.
Why Join Menlo
You will build the data engine behind a humanoid robot that ships to the real world. The datasets you deliver directly shape what Asimov can do. If you want ownership over a function from zero, and you would rather build systems than sit in status meetings, this is the place.
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.
