Staff Machine Learning Engineer - Foundation Model

Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.

Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).

Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.

Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).

Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.

Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.

Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or related field, with 3+ years of experience in deep learning research or productization.

Strong proficiency in PyTorch and modern transformer-based model design.

Experience in large-scale pretraining or multi-modal modeling (vision, language, or planning).

Deep understanding of representation learning, temporal modeling, and self-supervised or reinforcement learning techniques.

Familiarity with distributed training (DDP, FSDP) and large-batch optimization.

PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.