{"id":2750177,"url":"https://alion.io/job/zoox-seniorstaff-software-engineer-ml-training-performance","title":"Senior/Staff Software Engineer, ML Training Performance","company":{"id":3626,"name":"Zoox","domain":"zoox.com","url":"https://alion.io/company/zoox","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":72,"open_postings":42,"ghost_share":0,"stale_share":0.905,"repost_share":0,"time_to_fill_p50_days":85,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Foster City, United States","Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":254000,"max":350000,"currency":"USD","period":"year","gross":null,"usd_annual":350000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"GRPO","optional":false},{"name":"JAX","optional":false},{"name":"Megatron-LM","optional":false},{"name":"NCCL","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"TPU","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"VLM","optional":false},{"name":"XLA","optional":false}],"status":"live","first_seen_at":"2026-10-16T19:45:44Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-18T05:38:12Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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.\nThe Opportunity \n Are 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 our past talks at re:Invent.\nIn this role, you will:\nLead 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.\nOwn the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization for driving foundation models.\nDrive profiling and root-cause analysis of throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.\nPartner with ML researchers, data engineers, and hardware engineers to define requirements and make architectural decisions for large-scale training infrastructure.\nBuild custom kernels and benchmarks that track and validate performance gains across the stack.\nMentor engineers and raise the team’s technical bar through design reviews and best practices.\nQualifications\nNote: You do not have to meet all the requirements below to be considered for this position:\n7+ years of total experience, including 4+ years on large-scale model training platforms, with a record of leading projects end to end.\nDeep experience with distributed training frameworks (Megatron-LM, TorchTitan) and efficiently utilizing GPU/accelerator clusters at scale.\nExperience with RL frameworks (Verl/MILES) and algorithms (GRPO, PPO).\nExperience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.\nStrong command of profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to find and resolve training bottlenecks.\nProficiency in Python or C++.\nExperience with ML compilers, performance tooling, or hardware/software co-design (GPU/TPU/accelerator architectures).\nAbility to influence across teams and communicate technical tradeoffs to both researchers and 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