Senior/Staff Software Engineer, ML Training Optimization

The role

You will own ML training optimization for the models behind autonomous driving. You will set technical direction for distributed training, parallelism and RL on accelerators, and mentor engineers while working with the Autonomy teams.

What you will do

• Lead design, implementation and operation of training performance optimizations 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
• Drive profiling and root-cause analysis of throughput bottlenecks across the compute, compiler, framework and cluster-networking stack
• Partner with ML researchers, data engineers and hardware engineers on requirements and architectural decisions for large-scale training infrastructure
• Build custom kernels and benchmarks that track and validate performance gains across the stack
• Mentor engineers and raise the team's technical bar through design reviews and best practices

Required qualifications

• 7+ years of total experience, including 4+ years on large-scale model training platforms, with a record of leading projects end to end
• Deep experience with distributed training frameworks (Megatron-LM, TorchTitan) and efficiently using GPU/accelerator clusters at scale
• Experience with RL frameworks (Verl/MILES) and algorithms (GRPO, PPO)
• Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training
• Strong command of profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling)
• Proficiency in Python or C++
• Experience with ML compilers, performance tooling, or hardware/software co-design (GPU/TPU/accelerator architectures)
• Ability to influence across teams and communicate technical tradeoffs to researchers and engineers

Before you apply

• You do not have to meet all the listed requirements to be considered

About the company

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem to bring it to market. It works at the intersection of robotics, machine learning and design to provide mobility-as-a-service in urban environments.