Scientist I, Computational Protein Generation

at  Generate Biomedicines

ABOUT GENERATE:BIOMEDICINES

Generate:Biomedicines is a new kind of therapeutics company – existing at the intersection of machine learning, biological engineering, and medicine – pioneering Generative Biology™ to create breakthrough medicines where novel therapeutics are computationally generated, instead of being discovered. The Company has built a machine learning-powered biomedicines platform with the potential to generate new drugs across a wide range of biologic modalities. This platform represents a potentially fundamental shift in what is possible in the field of biotherapeutic development.
We pursue this audacious vision because we believe in the unique and revolutionary power of generative biology to radically transform the lives of billions, with an outsized opportunity for patients in need. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!
Generate:Biomedicines was founded in 2018 by Flagship Pioneering and has received nearly $700 million in funding, providing the resources to rapidly scale the organization. The Company has offices in Somerville and Andover, Massachusetts with over 300 employees.

We are seeking a creative, motivated Computational Scientist with a strong background in Protein Design. The ideal candidate would have demonstrated extensive expertise in computational protein design that goes beyond mastery of existing tools and methods, with strong intuitions about what is lacking in current approaches and an urge to fundamentally change existing paradigms. Exposure to deep machine learning techniques in the context of protein design is a strong preference.
The successful candidate will work with the Machine Learning, Protein Sciences and Preclinical Discovery and Development groups at Generate:Biomedicines to develop, analyze and apply protein design protocols toward specific design objectives, incorporating both existing and new methods and models. The role will involve building protein-generation algorithms, validating them using in-house and external data, designing novel proteins, and working closely with experimental scientists in a tightly integrated design-build-test-learn cycle with the opportunity to design agents that can fundamentally impact the lives of future patients.