Senior/Staff Software Engineer, ML Training Optimization

The Opportunity

Are 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.

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.
• 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 to define requirements and make 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.

Qualifications

*Note: You do not have to meet all the requirements below to be considered for this position:*

• 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 utilizing 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) to find and resolve training bottlenecks.
• 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 both researchers and engineers.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.