Engineering Manager, ML Performance Optimization
Foster City, CA
- Pay
USD 276,000–343,000/year — pay source
Salary 276,000 – 343,000 USD per year About Zoox
Read the full posting- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDrive end-to-end performance optimization for large-scale model training and inference
Build and run the ML platform for training, serving, and monitoring models
Deliver low-latency on-vehicle inference within real-time and compute budgets
Develop and execute the ML training and inference performance roadmap
Hire and mentor engineers, giving them growth opportunities and clear feedback
From the employer’s posting
Drive end-to-end performance optimization for large-scale model training and inference
Lead the design, implementation, and operation of a robust and efficient ML platform to enable the training, validation, serving, optimization and monitoring of ML models.
low-latency on-vehicle inference that meets strict real-time and compute budgets
Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization
Enable engineers on the team to grow their careers by providing the right opportunities and clear, timely feedback.
What you’ll bring
All qualificationsCore experience
- 8+ years of relevant experience, including 3+ years of management experience managing engineers.
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases.
- Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training.
- Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
Qualification wording
8+ years of relevant experience, including 3+ years of management experience managing engineers.
Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases.
Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training.
Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
Tools in this posting
- PyTorch
Source — Tool mentions in context
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases. - Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training. - Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
About Amazon
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 7, 2026 · 22 days ago
- Recorded sightings
- 52
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
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Job description
In this role, you will:
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Vision: Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization, ensuring scalability, reliability, and performance to support autonomous driving.
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Technical acumen: Lead the design, implementation, and operation of a robust and efficient ML platform to enable the training, validation, serving, optimization and monitoring of ML models.
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ML Performance Optimization: Drive end-to-end performance optimization for large-scale model training and inference, including distributed training efficiency, GPU utilization, memory and communication optimization, model compression (quantization, pruning, distillation), and low-latency on-vehicle inference that meets strict real-time and compute budgets.
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Hiring: Attract, hire, and inspire a diverse world-class engineering team, fostering a culture of innovation, collaboration, and excellence.
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Partnership: Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
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Mentorship: Enable engineers on the team to grow their careers by providing the right opportunities and clear, timely feedback.
Qualifications
- 8+ years of relevant experience, including 3+ years of management experience managing engineers.
- Strong technical background in ML performance optimization, such as distributed training strategies (data, tensor, pipeline parallelism, FSDP/ZeRO), mixed-precision training, kernel-level optimization (CUDA, Triton), compiler stacks (torch.compile, XLA, TVM), quantization, and profiling/benchmarking across GPU and embedded accelerators.
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases.
- Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training.
- Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
- Proven track record of extensive cross-functional collaboration, partnering with research, product, hardware, and platform teams to align priorities, influence technical direction, and deliver measurable performance improvements across organizational boundaries.
Salary
276,000 – 343,000 USD per year
Employment type
Full-time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 276,000 – 343,000 USD per year About Zoox
- Location & working pattern
Foster City, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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