Senior/Staff Software Engineer, ML Training Performance

Foster City · Seattle, WA · USD 254,000-350,000 / year · 4+ years experience · Full-time · Onsite · Posted Oct 16, 2026

At a glance

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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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.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
- 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.

Zoox is seeking a Senior or Staff Software Engineer to lead ML Training Performance optimizations for autonomous driving foundation models.
The ML Platform team builds and operates base layers of ML tools, model development, and serving systems for applied research teams.
This role involves driving ML performance optimization initiatives, working with state-of-the-art accelerators, and collaborating closely with autonomy teams.

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.

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.

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