Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models

Lead the design of end-to-end VLA architectures, bridging multi-modal perception with high-level linguistic reasoning and precise action generation.

Drive R&D in generative world models (latent dynamics) to create high-fidelity, controllable driving simulations for closed-loop training and evaluation.

Apply Advanced RL (Online/Offline) and IL to refine driving policies, focusing on long-horizon planning and complex multi-agent interactions.

Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training at a multi-billion parameter scale.

Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale.

Deep understanding of Sequential Decision Making, World Models, or Policy Gradient methods.

Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.).

A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles.