Senior/Staff Software Engineer, ML Training Performance
TECHNICAL STACK · 4 TAGS
OVERVIEW
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 performance 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.
- 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
- 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.
REQUIREMENTS
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.
QUESTIONS AND ANSWERS
- How much does the Senior/Staff Software Engineer, ML Training Performance at Zoox pay?
- The posting lists a range of $254K–$350K per year. Ranges reflect what Zoox publicly declared on the source posting.
- Where is this Senior/Staff Software Engineer, ML Training Performance role based?
- The role is based in Foster City, CA.
- What experience does Zoox expect for this role?
- The posting is tagged as a lead-level role, typically 7+ years of experience. Check the requirements section for specifics.
- Where is Zoox headquartered?
- Zoox is headquartered in Foster City, USA.
- How was this posting sourced?
- This role was pulled directly from Zoox's Lever careers site. Apply links open in the employer's own ATS — no reposts or aggregator middleware.
Apply links open in the employer's official ATS. Always verify recruitment messages on the company's careers page before sharing personal information.