Who We Are

Flagship Pioneering conceives, resources, and builds machine-learning enabled companies across both human health and sustainability. Flagship has created over 100 scientific ventures resulting in >$200 billion in aggregate value, 500+ issued patents, and >50 clinical trials, spanning Moderna TherapeuticsGenerate BiomedicinesIndigo AgTessera Therapeutics, and others. We harness science and entrepreneurialism to envision alternative futures, beginning with seemingly unreasonable propositions and navigating to transformational outcomes through an iterative, evolutionary methodology.

We are looking for extraordinary Machine Learning Scientists to work alongside individuals within the Flagship Ecosystem focused on solving the most impactful challenges in AI across both human health and sustainability. We collaborate, encourage failure, trust one another, and celebrate successful solutions to hard problems. We respect the diversity of opinion - because we value the freedom to explore hunches.

Position Summary

We believe deep integration of data-driven machine learning with experimental approaches is a core driver of the next generation of defining companies in health. We imagine this will be driven by individuals from diverse scientific and machine learning backgrounds. Thus, we are open to all profiles with computational excellence.

We are seeking the most innovative and entrepreneurial Machine Learning Scientists. You will join organizations at the early stages of our company creation process to develop innovative algorithmic methods, leveraging both in-house and external data to train and evaluate models while also deploying new algorithms into production and integrating deeply into experimental platforms to close feedback loops. The successful candidate will work closely with experimental scientists to rapidly advance various scientific programs.

Key responsibilities:

  • Develop or fine-tune deep learning architectures and hone them through deployment on experimental platforms.
  • Work with experimental groups to integrate modeling efforts into high-impact applications.
  • Develop production-quality code in a team setting and plan 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.


  • PhD in computer science with a desire to collaborate with leading experimentalists or a PhD in scientific field plus demonstrated experience applying deep learning. Exceptional candidates without PhDs will be considered.
  • Experience developing, debugging, and applying models using modern deep learning frameworks on GPUs in cloud environments.
  • Proficiency in Python and machine learning frameworks such as TensorFlow, Pytorch, and/or JAX.
  • Desire to work across the entire data stack, from data ingest to model deployment.
  • Curiosity and humility to work with scientific domain experts to identify and frame problems worth solving beyond existing benchmarks.
  • Energetic self-starter with the ability to work effectively in a startup environment.
  • Excellent analytical skills and ability to synthesize & communicate complex information rapidly and effectively.

Location: Cambridge, MA 

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.

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