FL120 | Cambridge, MA (Senior) Scientist, Machine Learning

At Flagship Pioneering, we create companies from first principles. Within Flagship Labs, small founding teams define new technical theses, test them rapidly, and build ventures around breakthrough ideas. We are forming a machine learning team inside a newly launched venture, Flagship Labs 120. Our work focuses on extracting latent structure from information‑rich measurements of complex physical systems—often requiring mechanism‑informed modeling, thoughtful inductive bias design, and principled approaches to inverse problems. This is a zero‑to‑one role focused on modeling innovation rather than routine optimization. You’ll design, prototype, test, and refine new approaches that help define the technical foundation of a platform from day one. What You’ll Do Develop and iterate on ML models for complex measurement data, from representation design through validation Design objectives and architectures that respect known constraints, symmetries, or latent structure in the data Explore and compare modeling strategies, balancing strong baselines with more experimental approaches when appropriate Investigate model behavior and failure modes to improve robustness and interpretability Collaborate closely with experimental and technical teammates to align modeling with data generation Contribute to shaping the long‑term ML strategy and technical direction of a new venture Who You Are You may come from physics, applied mathematics, engineering, computer science, or another quantitative field. You have hands‑on experience developing machine learning models—ideally in deep learning, representation learning, probabilistic modeling, or related areas. You are comfortable implementing and modifying models, training them end‑to‑end, and working directly with real data. Think algorithmically and reason from underlying structure Are comfortable adapting or extending model architectures when needed Have built and debugged meaningful ML systems or research prototypes Enjoy operating in dynamic, early‑stage environments Read papers, build prototypes to test ideas, and translate concepts into working systems What matters most is your ability to reason across data, models, and the systems they represent. Technical Background Required Strong hands‑on experience building and training modern ML models Fluency in Python and at least one major ML framework (e.g., PyTorch or equivalent) Experience working with real‑world or experimentally generated data Ability to design, run, and interpret ML experiments Comfort working in practical development environments (e.g., cloud infrastructure, experiment tracking, reproducible workflows) Helpful Experience with inverse problems, latent‑variable inference, or structured generative modeling (e.g., diffusion or flow‑based methods) Familiarity with geometric or symmetry‑aware architectures Experience incorporating physical or structural constraints into learning systems Experience working with time‑series or high‑dimensional signal data Exposure to biology, chemistry, physics, or related sciences We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship. #J-18808-Ljbffr Flagship Ventures