Senior/Staff Software Engineer, ML Training Optimization

Zoox is on a mission to reimagine transportation and build ground-up autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We’re still early in deploying on public roads, so this is a great time to join and have a significant impact. The ML Platform team enables innovation in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.

The OpportunityAre you excited to own ML training optimization at Zoox and make the models that power autonomous driving train faster, cheaper, and at greater scale? You’ll set technical direction for distributed training, parallelism strategies, mixed-precision training, and RL on SOTA accelerators, especially for multi-modal VLA and VLM models.

You’ll partner with all Autonomy teams (Perception, Prediction, Planner, Simulation, Collision Avoidance) and act as a force multiplier for our internal customers, while mentoring engineers and shaping the team’s roadmap as we expand into new ML domains. If you want to learn more about our stack behind autonomous driving, please lookhere. If you want to learn more about our ML Infrastructure, here is one of ourpast talks at re:Invent.

In this role, you will: Lead the design, implementation, and operation of training performance optimizations (data/tensor/pipeline parallelism, mixed precision, gradient checkpointing, GPU kernels) to scale VLM, VLA, and Foundation model training. Own the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization for driving foundation models.