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· Experience with multi-GPU or distributed training is a plus · Ability to work independently as well as part of an interdisciplinary team in a fast-paced environment, while making necessary connections
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start
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into a cloud environment. Minimum two to three years of experience using PyTorch or Tensorflow, including optimizing code for GPU clusters Experience building advanced GenAI workflows such as retrieval
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of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing