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