Software Engineer, ML Performance Optimization
Drive ML performance optimization initiatives to make autonomous driving models faster and more efficient using distributed training, quantization, distillation, and profiling tools.
About the job
Responsibilities
- Design, implement, and operate cutting-edge ML Training OR Inference performance optimization techniques to scale VLM, VLA, and Foundational models and deploy them efficiently in robotaxis.
- Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
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++.
Nice-to-Haves
- Experience with distributed training techniques, quantization, distillation, and pruning.
- Work with SOTA accelerators and inference optimization frameworks.
Skills
PyTorch, TensorRT, Python, C++, Nvidia Nsight, Pytorch Profiler, Distributed Training, Quantization, Distillation, Pruning
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