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.
