In this role, you will:

• 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 OR Inference performance optimization techniques to scale our VLM, VLA, and Foundational models and deploy them efficiently in our robotaxi.
• 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.

Qualifications

• Strong 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 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.

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.