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, Mechanical or Electrical Engineering, Control, Applied Mathematics, or a related field. You should have a solid foundation in machine learning and strong programming skills (Python, deep learning
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released models Make evaluations run quickly and at scale: parallel execution on Alps, efficient use of inference backends, caching, and result tracking Reduce mismatch between evaluations during
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/aarch64 platforms Familiarity with HPC networking and communication stacks: Slingshot, libfabric, NCCL and its debugging Experience with parallel filesystems (e.g. Lustre) and storage performance tuning
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