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Position: Machine Learning Scientist
Institution: Cellarity
Location: Cambridge, Massachusetts, United States
Duties: Develop or fine-tune deep learning architectures and hone them through deployment on experimental platforms; Work with experimental groups to integrate modeling efforts into high-impact applications; Develop production-quality code in a team setting and plan for deploying and training models at scale; Present progress from scientific work in regular research meetings and prepare reports and slide decks for broader internal and external communication
Requirements: PhD in computer science with a desire to collaborate with leading experimentalists or a PhD in scientific field plus demonstrated experience applying deep learning. Exceptional candidates without PhDs will be considered; Experience developing, debugging, and applying models using modern deep learning frameworks on GPUs in cloud environments; Proficiency in Python and machine learning frameworks such as TensorFlow, Pytorch, and/or JAX; Desire to work across the entire data stack, from data ingest to model deployment
   
Text: Machine Learning Scientist Develop or fine-tune deep learning architectures and hone them through deployment on experimental platforms; Work with experimental groups to integrate modeling efforts into high-impact applications; Develop production-quality code in a team setting and plan for deploying and training models at scale; Present progress from scientific work in regular research meetings and prepare reports and slide decks for broader internal and external communication PhD in computer science with a desire to collaborate with leading experimentalists or a PhD in scientific field plus demonstrated experience applying deep learning. Exceptional candidates without PhDs will be considered; Experience developing, debugging, and applying models using modern deep learning frameworks on GPUs in cloud environments; Proficiency in Python and machine learning frameworks such as TensorFlow, Pytorch, and/or JAX; Desire to work across the entire data stack, from data ingest to model deployment
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