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 an Autonomy Engineer focused in VLA Pre-training, you will work on all aspects of training capable policies. You'll pre-train base models on a diverse, multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks, shape data collection processes, and explore effective ways to generate and use synthetic data.

This is primarily a deep learning role, so we're looking for experience solving real-world problems with modern neural networks. Robotics experience isn't strictly required, but if you're coming from outside the field, be prepared to get up to speed on a new domain quickly.

What You'll Do

• Post-train policies via behavior cloning and RL; own the full loop from data to deployment.

• Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.

• Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.

• Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.

• Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.

• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.

What We're Looking For

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.

• Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.

• Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.

• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.

• Familiarity with modern software engineering practices.

• You document experiments clearly and communicate trade‑offs crisply.

Nice to have

• Robotics or autonomous driving experience.

• Experience applying RL to LLMs or robotics.

• Experience with VLA (vision-language-action) models.

• Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).

• Publications at top-tier deep learning conferences or equivalent open‑source contributions.

• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks.

What We Offer

• Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.

• Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.

• 401(k) retirement plan with employer match.

• Equity included–we believe builders should share in what they build.

• Free daily catered lunch, snacks, and drinks in‑office.

• Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.

• Freedom to influence the product and own key initiatives.

For this role in California, the expected base salary range is $180,000–$300,000 USD per year; your placement in that range depends on how your experience maps to our internal leveling.