In this role, you will:
Design, implement, and operate cutting-edge ML training performance optimization techniques — distributed training (data/tensor/pipeline parallelism), mixed-precision, gradient checkpointing, and GPU kernels — to scale our VLM, VLA, and Foundational models training efficiency.
Work on RL framework and RL performance optimization for driving foundation model
Profile and debug training throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
Collaborate closely with ML researchers, data engineers, and hardware engineers to define requirements and align on architectural decisions for large-scale training infrastructure.
Implement custom kernels and build benchmarks to track and validate training-performance optimizations across the stack.
Qualifications
4+ years of total experience, including 2+ years working on large-scale model training platforms.
Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.
Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)
Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.
Proficient in Python or C++.
Experience with ML compilers, performance tooling, or hardware/software co-design (e.g., GPU/TPU/accelerator architectures).
Note: You do not have to meet all the requirements below to be considered for this position:
Skills Required
- 4+ years of total professional experience
- 2+ years working on large-scale model training platforms
- Experience with distributed training frameworks such as Megatron-LM or TorchTitan
- Experience efficiently utilizing GPU or accelerator clusters at scale
- Experience with reinforcement learning frameworks such as Verl or MILES and algorithms including GRPO or PPO
- Experience with collective communication libraries such as NCCL and optimizing communication-compute overlap in multi-node training
- Experience with profiling tools such as NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling
- Proficiency in Python or C++
- Experience with ML compilers, performance tooling, or GPU, TPU, and accelerator architectures
Zoox Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Zoox and has not been reviewed or approved by Zoox.
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Healthcare Strength — Healthcare is extensive, with broad medical and vision options, company‑paid disability coverage, and multiple mental‑health resources. Feedback suggests coverage breadth and auxiliary programs support a wide range of needs.
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Parental & Family Support — Family supports include paid parental leave, additional pregnancy disability time, fertility coverage, and adoption/surrogacy assistance. Backup care and family‑oriented programs further reinforce support across life stages.
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Wellbeing & Lifestyle Benefits — Day‑to‑day perks are robust, including free daily meals, fitness subsidies, commuter support, and on‑site amenities. Feedback suggests these lifestyle benefits enhance convenience and workplace experience, especially for office‑based roles.
Zoox Insights
What We Do
Zoox is an autonomous mobility company that was founded to provide a safer, cleaner, and more enjoyable future on the road. To achieve that goal, the company has spent the past 10 years creating a purpose-built robotaxi that gives the world a better way to ride.
Why Work With Us
At Zoox, we are working to solve one of the greatest technological challenges of our generation. From the beginning, we have been focused on our goal of reimagining transportation from the ground up. We are a mission-driven community of innovators working together to create a safer, cleaner, and more enjoyable future on the road.
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