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Zoox is building autonomous robotaxis from the ground up and is seeking a Senior/Staff Software Engineer to lead ML training performance optimization initiatives. The ML Platform team enables large-scale foundation models, vision language models (VLMs), and vision language action models (VLAs) to power autonomous driving.
In this role, you will lead the design, implementation, and operation of training performance optimizations including data/tensor/pipeline parallelism, mixed precision, gradient checkpointing, and GPU kernel development to scale VLM, VLA, and foundation model training. You will own the technical strategy and multi-quarter roadmap for reinforcement learning (RL) frameworks and RL performance optimization for driving foundation models. You will drive profiling and root-cause analysis of throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
You will partner with ML researchers, data engineers, and hardware engineers to define requirements and make architectural decisions for large-scale training infrastructure. You will build custom kernels and benchmarks that track and validate performance gains across the stack. Additionally, you will mentor engineers and raise the team's technical bar through design reviews and best practices.
This is an opportunity to work with state-of-the-art accelerators and cutting-edge techniques, collaborating closely with Autonomy teams including Perception, Prediction, Planner, Simulation, and Collision Avoidance. The team operates the base layer of ML tools, model development, and serving systems used by applied research teams for in- and off-vehicle ML use cases, with significant growth opportunities as robotaxi deployments expand.
REQUIREMENTS:
- 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
Note: You do not need to meet all requirements to be considered.
