Software Engineer, ML Performance Optimization
Foster, CA - USA
Job Summary
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.
Collaborate closely with cross-functional teams including ML researchers software engineers data engineers and hardware engineers to define requirements and align on architectural decisions.
- 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 NVIDIAs Nsight or PyTorchs Profiler for identifying model training and serving bottlenecks.
- Proficient in Python or C.
Base Salary Range
There are three major components to compensation for this position: salary Amazon Restricted Stock Units (RSUs) and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling as well as positioning within a level is determined by a range of factors including but not limited to a candidates relevant years of experience domain knowledge and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave vacation bereavement) unpaid time off Zoox Stock Appreciation Rights Amazon RSUs health insurance long-term care insurance long-term and short-term disability insurance and life insurance.
Required Experience:
IC
About Company
We’re reinventing personal transportation—making the future safer, cleaner, and more enjoyable for everyone. This is on-demand autonomous ride-hailing.