Supports planning and organization of research projects in Human AI - Interaction at Fraunhofer Institute. Involves interdisciplinary teamwork and various research methodologies.
About the role
- 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.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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