Senior/Staff Software Engineer, ML Performance Optimization

Zoox is on a mission to reimagine transportation by building autonomous robotaxis. The role involves leading the ML Performance Optimization initiatives to enhance the Training and Inference platform for autonomous driving, collaborating with various teams to unlock ML innovation.

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 x-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

Requirements:

• 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's Nsight or PyTorch's 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
• 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