Senior​/Software Engineer; ML Optimization

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

• You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and Advanced Hardware Engineering group and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox
• We build and operate the base layer of ML tools, deep learning frameworks, and inference systems used by our applied research teams for in- and off-vehicle ML use cases
• You will lead a team of strong software engineers and act as a force multiplier for our internal customers
• This team has a lot of growth opportunities as we expand our robotaxi deployments and venture into new ML domains
• 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