ML Engineer, Foundation Models

Design and iterate on our VLA model architecture—including the VLM backbone, action decoder, and multimodal fusion pipeline

Build and optimize large-scale training infrastructure (distributed training, data pipelines, mixed-precision, efficient fine-tuning)

Develop simulation-based evaluation and closed-loop training workflows using photorealistic neural rendering

Curate and manage multimodal training datasets spanning real-world driving and synthetic scenarios

Translate state-of-the-art research (diffusion/flow-matching action heads, reasoning-augmented VLAs, world models) into production-grade systems

Collaborate directly with vehicle systems and controls engineers to integrate model outputs into a real-time autonomous driving stack

MS or PhD in Computer Science, Machine Learning, Robotics, or a related field—or equivalent industry experience

Strong proficiency in PyTorch, distributed training, and GPU-accelerated workflows

Solid foundation in transformer architectures, attention mechanisms, and modern generative modeling (diffusion, flow matching)

Eligible to work in the United States

Experience building or contributing to end-to-end autonomous driving systems

Track record of publications at top ML/robotics venues (NeurIPS, ICLR, ICRA, CoRL) or significant open-source contributions

Familiarity with sim-to-real transfer, photorealistic simulation, or neural rendering for driving scenes

Experience with reinforcement learning, imitation learning, or learning from demonstration in embodied settings

Comfort operating as an early team member—high ownership, low ego, fast iteration