Drive ML performance optimization initiatives to make autonomous driving models faster and more efficient using distributed training, quantization, distillation, and profiling tools.
192k – 257k/yr
On-site4+ YOEML Engineering
About the role
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.
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190k – 250k/yr
Hybrid5+ YOEML Engineering
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SesameSan Francisco, CA +2
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