Director of Autonomy - Biped
- Compensation
- Not disclosed
- Location
- US, San Diego, CA
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Not specified
- Posted
Skills
Must have
- 5+ years working on robots (industry or research) with shipped artifacts to show for it, including learned manipulation or locomanipulation policies deployed on real hardware.
- Experience leading a team of 5+ engineers doing applied robot learning, including hiring and people management, with capabilities delivered to a deadline.
- Hands-on today: you've trained and debugged policies within the last year and intend to keep doing so, and can curate data, post-train a policy, and diagnose failures without delegating that work.
- Experience post-training VLA-class policies (autoregressive, diffusion, or flow-matching based) via behavior cloning and/or RL.
- Solid understanding of the modern teleoperation and low-level control stack — whole-body control, inverse kinematics, RL locomotion controllers — and the ability to tell whether a failure lives in the
- Familiarity with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training, PyTorch or JAX.
- Strong grounding in modern software engineering practices, with the ability to document experiments clearly and communicate trade-offs crisply to engineers and leadership.
Nice to have
- Experience with multi-fingered dexterous hands: teleoperation and retargeting, tactile sensing, in-hand manipulation.
- Experience with legged platforms: bipedal locomotion, whole-body control, humanoid teleoperation.
- Experience applying RL to robotics problems in simulation or on real robots.
- Familiarity with Physical Intelligence (π) models, NVIDIA GR00T, or similar open VLA frameworks.
- Publications at top-tier robotics or deep learning conferences (CoRL, RSS, ICRA, NeurIPS) or equivalent open-source contributions.
Full description
Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.
About The Role
As Director of Autonomy at Humanoid, you will lead the team that teaches our bipedal platform to perform locomanipulation tasks in homes and service environments: tidying a room, loading and unloading a dishwasher, putting groceries away, folding laundry, and similar tasks. The output of your team is capabilities that run reliably on real robots and pass our evals, not papers or one-off demos. This is a player-coach role: you own the roadmap, hiring, and delivery, while also training policies, curating data, and debugging failures on the robot yourself. That hands-on work is how you lead: it's how you find what slows the team down and fix it for everyone.
This role sits at the intersection of applied deep learning, robotics, and people leadership, working closely with the Controls team on whole-body teleoperation and combining RL controllers with learned policies, and with the Core Technology team on foundation model pre-training for five-finger dexterity and RL methods. If you have hands-on experience with real robot hardware, a track record in applied deep learning, and have led engineers doing this kind of work, we want to hear from you.
What You'll Do
- Own The Capability Roadmap: Work with product to identify use-case priority, choose the execution strategy, define the evals, and deliver on the agreed timeline.
- Lead The Team: Hire, develop, and manage a team of robot learning engineers; set engineering and experimental standards; run day-to-day prioritization.
- Co-Own The System 0/1 Interface: Agree with the Controls team on what the policy commands (end-effector targets, root velocity, pelvis height, foot targets, hand joint targets), debug failures across that boundary, and push requirements into the whole-body control roadmap.
- Partner On Data Strategy: Specify what good data looks like for bipedal locomanipulation with data collection and external acquisition organizations, ensure diversity and coverage, iterate on operator instructions, and supervise collection on the biped.
- Drive Sim-To-Real Transfer: Work with the Simulation team to set up RL training on the digital twin for bipedal tasks, then iterate on reward and simulation quality to ensure transfer to the real world.
- Shape Base Model Development: Feed bipedal requirements — embodiment coverage in the pretraining mixture, action representations, latency budgets — into the VLA Core Technology team's base model development, and adopt new base models as they land.