ML tooling engineer at GM developing tools for analyzing and optimizing ML models. Collaborating with AV Engineering teams to enhance internal ML tooling for performance improvements.
Responsibilities
Identify new opportunities to improve both training and inference efficiency
Build workflows for correctness and performance analysis on physical in-car compute and sensors
Build tooling to predict model performance based on architecture and data shape
Build tooling to trace actual performance on large distributed training and distillation jobs
Continually evolve the toolchain and stack, to leverage the latest advancements in AI
Influence model architecture decisions and strategy within GM
Requirements
5+ years of experience in the field of AI/ML
Experience with ML frameworks (e.g., PyTorch, TensorFlow) and NVIDIA developer ecosystem (TensorRT, Nsight-systems, Nsight-compute)
Expertise in writing production quality Python/C++ code
Expertise in the software development life-cycle - coding, debugging, optimization, testing, integration
BS, or higher degree, in CS/CE/EE, or equivalent
Experience developing and deploying machine learning models
GPU programming (CUDA) and familiarity with ML SW stack (e.g., cuDNN, cuBLAS)
Experience with ML accelerators and hardware architecture
Benefits
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.
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