Software Engineer, ML Performance Optimization

Job description

Job responsibilities

• Design, implement, and operate 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 cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.

Job requirements

• 4+ years of total experience, including 2+ years of working on large-scale model training or inference platforms.
• 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 or C++.