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
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Post-train policies via behavior cloning and RL; own the full loop from data to deployment.
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Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.
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Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.
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Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.
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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.
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Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.
What We're Looking For
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3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
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Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
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Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
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Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
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Familiarity with modern software engineering practices.
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You document experiments clearly and communicate trade‑offs crisply.
Nice to have
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Robotics or autonomous driving experience.
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Experience applying RL to LLMs or robotics.
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Experience with VLA (vision-language-action) models.
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Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).
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Publications at top-tier deep learning conferences or equivalent open‑source contributions.
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Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks.
What We Offer
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Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.
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Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.
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401(k) retirement plan with employer match.
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Equity included–we believe builders should share in what they build.
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Free daily catered lunch, snacks, and drinks in‑office.
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Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
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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.
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