(Senior) Machine Learning Scientist
Flagship Pioneering, Inc.
Summary
We are hiring a Machine Learning Scientist to train, validate and ship the predictive models that power our molecular design platform. You will build models that score chemical molecules for developability (ADMET), potency, synthesizability and 3D structure, working alongside chemists, biologists and platform engineers. In your first year, success means two or three models in production use by project teams, each with documented benchmarks, calibrated uncertainty and a clear applicability domain.
Job description
About the role
We are hiring a Machine Learning Scientist to train, validate and ship the predictive models that power our molecular design platform. You will build models that score chemical molecules for developability (ADMET), potency, synthesizability and 3D structure, working alongside chemists, biologists and platform engineers. In your first year, success means two or three models in production use by project teams, each with documented benchmarks, calibrated uncertainty and a clear applicability domain.
What you will do
- Train and maintain ML models for ADMET and developability endpoints such as solubility, permeability, metabolic stability, toxicity and aggregation risk.
- Build potency and binding models (QSAR, ligand- and structure-based) to rank and prioritize molecules against targets.
- Develop synthesizability models: synthetic accessibility scoring, reaction-outcome prediction and retrosynthesis-informed filters.
- Apply and fine-tune structure-aware models (GNNs, equivariant networks, protein-ligand structure and docking-informed ML).
- Curate and clean assay data, define scaffold- or time-split benchmarks, and quantify uncertainty and applicability domain.
- Package models for the platform with engineers: versioned, reproducible and monitored for drift.
- Present results and limits of each model to chemists and project teams, and incorporate their feedback into retraining.
What you bring
- PhD in computational chemistry, cheminformatics, machine learning or a related field, or an MS plus equivalent industry experience (about 3-6 years of hands-on modeling).
- Demonstrated experience training and validating ML models on chemical or molecular data, in industry or academia, with shipped or published results.
- Direct experience with at least two of: ADMET/developability, potency/binding, synthesizability/retrosynthesis, protein or ligand structure.
- Fluency with molecular representations (fingerprints, descriptors, graphs, 3D) and tools such as RDKit.
- Strong Python and a deep learning framework (PyTorch preferred); experience with GNNs or transformers on molecules.
- Rigorous evaluation habits: scaffold or temporal splits, baselines, noisy-label handling and uncertainty estimation.
- Clear communication with chemists and biologists.
Nice to have
- Publications, open-source contributions or competition results in molecular ML.
- Experience with generative design, active learning or Bayesian optimization loops.
- Experience with large-scale training, MLOps and model serving.
- Familiarity with docking, free-energy methods or protein structure prediction tools.
- Experience working with biologics or non-small-molecule modalities.
Requirements
- PhD in computational chemistry, cheminformatics, machine learning or a related field, or an MS plus equivalent industry experience (about 3-6 years of hands-on modeling)
- Demonstrated experience training and validating ML models on chemical or molecular data, in industry or academia, with shipped or published results
- Direct experience with at least two of: ADMET/developability, potency/binding, synthesizability/retrosynthesis, protein or ligand structure
- Fluency with molecular representations (fingerprints, descriptors, graphs, 3D) and tools such as RDKit
- Strong Python and a deep learning framework (PyTorch preferred)
- Experience with GNNs or transformers on molecules
- Rigorous evaluation habits: scaffold or temporal splits, baselines, noisy-label handling and uncertainty estimation
- Clear communication with chemists and biologists
Benefits
- Healthcare coverage
- Annual incentive program
- Retirement benefits
Company overview
Flagship Pioneering is a pioneering biotechnology venture capital firm that specializes in conceiving, creating, and developing first-in-category life sciences companies. Founded in 2000 and headquartered in Cambridge, Massachusetts, the firm applies a unique hypothesis-driven innovation process to originate and foster scientific ventures with transformative potential for human health and sustainability. Flagship has originated over 100 scientific ventures since its inception, creating more than $30 billion in aggregate value across its portfolio. The firm's ecosystem includes notable companies such as Moderna Therapeutics, Denali Therapeutics, Evelo Biosciences, and Seres Therapeutics, among others. Flagship operates through a systematic entrepreneurship model that combines scientific creativity, technological ingenuity, and executive leadership to build platform companies capable of generating multiple breakthrough products. With over $3 billion in aggregate capital commitments, the firm invests primarily in therapeutics, health technologies, and sustainability sectors, working in partnership with academic institutions, pharmaceutical companies, and investment communities to advance groundbreaking science and innovation globally.