Staff Machine Learning Engineer - Foundation Model

What you'll do

• Design multimodal model architectures and develop pretraining and fine-tuning strategies using fleet data.
• Research cross-modal alignment and evaluate model behavior across perception, reasoning, and planning tasks.
• Scale distributed training and optimize models for onboard deployment, including quantization and latency–accuracy trade-offs.

What they ask for

• Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or a related field.
• 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 multimodal modeling, and familiarity with distributed training (DDP, FSDP).

Nice to have

• PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
• Top-tier AI conference publications or prior experience with foundation/end-to-end driving models, LLM/VLM architectures, or policy learning.

Pay

$215,280-$364,320 USD base salary

Tools

PyTorch, FSDP, DDP, ViT, Flamingo, BEVFormer, RT-2, GRPO

AI focus

Research, train, evaluate, and deploy multimodal vision-language-action foundation models for autonomous driving, using pretraining, fine-tuning, cross-modal alignment, imitation and reinforcement learning, and model deployment optimization.