# Staff Software Engineer, ML Performance Optimization

**Company:** [Zoox](https://hotfix.jobs/companies/zoox)
**Location:** Foster City, CA, Seattle, WA
**Role:** ML Engineering
**Salary:** $242k – $389k/yr
**Experience:** 4+ years
**Skills:** PyTorch, TensorRT, Ray Serve, Nvidia Nsight, Pytorch Profiler, Python, C++, GPU, Distributed Training, Model Compression
**Posted:** 2023-10-28

> Leads ML performance optimization for training and inference platforms in autonomous driving, collaborating across teams to enhance efficiency using PyTorch, TensorRT, and profiling tools. Requires strong Python/C++ skills, GPU expertise, and 4+ years in large-scale ML platforms; leads engineering team.

## Job Description

## Responsibilities
- Develop and execute a strategic vision for the ML Performance Optimization team to unlock ML innovation in autonomous driving and rider experience.
- Lead the design, implementation, and operation of cutting-edge ML Training and inference performance optimization techniques.
- Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
- Enable the engineers in the team to grow their careers by providing technical guidance and mentorship.

## Qualifications
- Strong experience with training frameworks like **PyTorch**, leveraging GPUs efficiently for distributed model training.
- Experience with GPU-accelerated inference using **TensorRT**, **Ray Serve**, or similar frameworks.
- Experience using profiling tools like **NVIDIA Nsight** or **PyTorch Profiler** for identifying model training and serving bottlenecks.
- Proficient in **Python** and **C++**.
- Experience with model compression techniques to reduce model size and improve performance.

## Bonus Qualifications
- 10+ years of total experience, including 4+ years of working on large-scale model training or inference platforms.
- Excellent leadership skills with a demonstrated ability to lead high-performing engineering teams.

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