Machine Learning Scientist, Large Language Models for Materials | Cambridge, Massachusetts, United States | Flagship Pioneering, Inc.

Position:	Machine Learning Scientist, Large Language Models for Materials
Institution:	Flagship Pioneering, Inc.
Location:	Cambridge, Massachusetts, United States
Duties:	Fine-tune, scale and deploy large language models over scientific and patent literature for knowledge extraction in materials synthesis and performance; Utilize and develop new prompt engineering strategies in the materials domain; Use LLM-backed agents for lab orchestration, design of experimental assays, and optimization of process parameters for materials synthesis and testing; Contribute to a digital platform that can continually finetune models as more data becomes available; Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, writing publications, and developing relationships with key opinion leaders; Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis
Requirements:	PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field; Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models; Coding experience with large language models (e.g. GPT or other autoregressive LLMs); Strong experience with prompt engineering; Hands-on experience implementing, deploying, evaluating, fine-tuning and hyperparameter-tuning deep learning models at scale; Experience in machine learning strategies like lifelong learning, online learning, incremental learning

Text:	Machine Learning Scientist, Large Language Models for Materials Fine-tune, scale and deploy large language models over scientific and patent literature for knowledge extraction in materials synthesis and performance; Utilize and develop new prompt engineering strategies in the materials domain; Use LLM-backed agents for lab orchestration, design of experimental assays, and optimization of process parameters for materials synthesis and testing; Contribute to a digital platform that can continually finetune models as more data becomes available; Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, writing publications, and developing relationships with key opinion leaders; Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field; Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models; Coding experience with large language models (e.g. GPT or other autoregressive LLMs); Strong experience with prompt engineering; Hands-on experience implementing, deploying, evaluating, fine-tuning and hyperparameter-tuning deep learning models at scale; Experience in machine learning strategies like lifelong learning, online learning, incremental learning