Scientist, Machine Learning; Language Models Materials

Flagship Labs 97 Inc. (FL97) is a privately held, early-stage company pioneering the application of artificial intelligence to transform all science. Our Physical Sciences effort is developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.

FL97 was founded by Flagship Pioneering, an innovation enterprise that conceives, creates, resources, and builds companies that invent breakthrough technologies to transform health care, agriculture, and sustainability. Flagship has created over 100 groundbreaking companies since 2000, including Moderna (NASDAQ: MRNA), Generate Biomedicines, Tessera Therapeutics, Indigo Ag, Inari Agriculture, and Sana Biotechnology.

• 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
• Work with the experimental team to drive material discovery and development
• Report findings to stakeholders and leadership in written reports and verbal presentations.

• 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.
• Strong experience in at least one ML framework (PyTorch/Tensor Flow/Jax) and robust experience in Python data science ecosystem (Numpy, Sci Py, Pandas, etc.)
• Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models
• PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
• Strong self-starter and independent thinker, with strong attention to detail
• Demonstrated industry experience or academic achievement
• Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
• Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting

• Experience using AWS services
• Experience with machine learning integration in experiment workflows

Flagship Pioneering is a biotechnology company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.