Autonomy Engineer - VLA Pre-training

About the Role

What You'll Do

What We're Looking For

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.

Qualifications

• 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.

Responsibilities

• 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.
• 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.