Software Engineer, ML Performance Optimization
TECHNICAL STACK · 3 TAGS
OVERVIEW
IN THIS ROLE, YOU WILL
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Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale our VLM, VLA, and Foundational models and deploy them efficiently in our robotaxi.
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Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
QUALIFICATIONS
- 4+ years of total experience, including 2+ years of working on large-scale model training or inference platforms.
- Experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.
- Experience with GPU-accelerated inference using TensorRT or similar frameworks.
- Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks.
- Proficient in Python or C++.
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 Software Engineer, ML Performance Optimization at Zoox pay?
- The posting lists a range of $192K–$257K per year. Ranges reflect what Zoox publicly declared on the source posting.
- Where is this Software Engineer, ML Performance Optimization 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.