Machine Learning Engineer

over 2 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

At Generate, we believe in the power of machine learning and protein engineering to dramatically improve human wellbeing. This means creating better medicines, accessible to more people, in a fraction of the time. We are building a flexible protein generation platform that has the potential to target diverse human diseases using a wide array of modalities (e.g., antibodies, peptides, enzymes, and more). At the heart of this is an iterative loop of machine learning-powered generation and high-throughput data collection to create medicines that are potent, safe, and impactful for patients.

We are searching for a skilled machine learning engineer to build and refine the core machine learning tooling and models that power our generation platform. They will collaborate closely with computational and life scientists to engineer new generalizable methods for generating functional therapeutic proteins. Their responsibilities will include keeping up and pushing forward the latest innovations around ML for proteins, probabilistic and generative modeling, and model-based optimization with uncertainty.

Key responsibilities:

Develop and productionize models and algorithms for data-driven protein generation and deploy them on our experimental platform.
Build machine learning tooling and library code that enables an efficient, flexible, and scalable platform.
Regularly translate and implement models from the machine learning literature into practice from scratch.
Maintain development of new skills in the area of applied machine learning as the field evolves.
Develop production-ready code in a team setting and work with MLOps for deploying and training models at scale.
Present progress from work in regular research meetings.

Qualifications:

M.S. in computer science, applied math, computational biology or equivalent industry experience.
Experience building, training, and evaluating machine learning models using strong software engineering principles.
Experience working with industrial scale models on distributed infrastructure (e.g. AWS).
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:

Practical experience with developing deep generative models (e.g., autoregressive models, VAEs, Flows, GANs, EBMs etc.) in an applied setting.
Demonstrated experience developing software in a team setting.
Worked with machine learning in a scientific context (e.g. protein design, high energy physics, small molecule design, computer vision for scientific applications, diagnostic machine learning)
Peer reviewed publication(s) in the area of machine learning
A strong orientation towards well written and structured code that is useful to others.

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.