Autonomy Engineer - VLA Pre-training

Job description

Job 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.

Job requirements

• 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.