Machine Learning Scientist

almost 3 years old

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Who We Are

Generate Biomedicines, Inc. is a Flagship backed, privately-held biotechnology company on a mission to reimagine the drug discovery process through the use of cutting-edge machine learning techniques. Core to Generate’s approach is the development and application of novel machine learning algorithms to solve foundational problems in molecular and protein biology. Generate’s unique platform seeks to drive innovation at the intersection of machine learning and biology through deep collaborations between wet lab and dry lab scientists and engineers. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!

Since its inception in 2018, Generate has received over $50 million in venture funding and its board of directors includes scientific and entrepreneurial luminaries such as Frances Arnlold (Nobel prize in chemistry, 2018), Stéphane Bancel (CEO, Moderna), and Noubar Afeyan (Founder and CEO, Flagship Pioneering). Generate was founded by Flagship Pioneering, a venture creation firm based in Cambridge, MA that conceives, creates, resources, and develops first-in-category life sciences companies to transform human health and sustainability. Since Flagship’s launch in 2000, the firm has applied a unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, including: Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY), Indigo Agriculture, and Sana Biotechnology.

Position Summary

Generation of novel proteins through data-driven machine learning models is at the core of Generate’s platform. We aim to upend the traditional approach to drug development towards one characterized by intentionality, surgical precision, and speed by developing methods for protein generation that can reliably generalize across biological functions, disease areas, and therapeutic modalities.

We are seeking creative, motivated Machine Learning Scientists to develop and apply our core technologies for ML-powered protein generation. They will join a vibrant and growing machine learning group at Generate to develop innovative methods for protein generation and modeling, leveraging both in-house and external data to train and evaluate models while also deploying new algorithms into production on our experimental platform. The successful candidate will work closely with experimental scientists from Protein Sciences and Medicines groups to rapidly advance the scientific program.

Key responsibilities:

Develop novel machine learning models and algorithms for data-driven generation of proteins, and hone them through deployment on our experimental platform.
Advance and evaluate the state of the art for machine learning models of protein sequence, structure, and function, including but not limited to protein sequence design, structure prediction, complex prediction, and function learning.
Use our integrated data platform to devise models able to leverage measured labels “in-the-loop”.
Work with Protein Sciences and Medicines groups to tailor modeling efforts toward high-impact therapeutic applications.
Develop production-quality code in a team setting and work with MLOps 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.

Qualifications:

PhD in Computational Biology, Computer Science, or a related field with demonstrated experience working on scientific applications
3+ years of experience with developing Machine Learning methods to solve scientific problems, with a particular interest towards applications to protein modeling as well as adjacent fields such as genomics, chemistry, immunology, or physics
Experience developing, debugging, and applying models using modern deep learning frameworks.
Proficiency in Python and experience analyzing data with Numpy/Scipy, R, or similar.

Nice to have:

Foundational knowledge around probabilistic machine learning and optimization methods
Practical experience developing deep generative models (e.g., autoregressive models, VAEs, Flows, GANs, EBMs etc.)
Publications in major ML conferences or scientific journals that apply ML to problems in molecular biology, structural biology, or genetics, especially at the intersection of machine learning and proteins.
Demonstrated experience developing software in a team setting.
Experience with optimizing performant code.

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.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.