(Senior) Machine Learning Scientist

Flagship Pioneering, Inc.•Watertown, MA

20h•$132,000 - $258,500

About The Position

Abiologics, Inc. is hiring a Machine Learning Scientist to train, validate, and deploy predictive models for its molecular design platform. The models will score chemical molecules for developability (ADMET), potency, synthesizability, and 3D structure, in collaboration with chemists, biologists, and platform engineers. Success in the first year means having two or three models in production use by project teams, each with documented benchmarks, calibrated uncertainty, and a clearly defined applicability domain. Abiologics is an early-stage biotechnology company developing Synteins, a class of macromolecular medicines enabled by synthetic and computational technologies. The company was founded in Flagship Pioneering’s venture creation engine.

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 machine-learning models on chemical or molecular data in industry or academia, with shipped or published results.
Direct experience in at least two of the following: ADMET/developability, potency/binding, synthesizability/retrosynthesis, or protein or ligand structure.
Fluency with molecular representations such as fingerprints, descriptors, graphs, and 3D representations, and tools such as RDKit.
Strong Python skills and experience with a deep-learning framework; PyTorch is preferred. Experience with GNNs or transformers on molecules.
Rigorous evaluation practices, including scaffold or temporal splits, baselines, noisy-label handling, and uncertainty estimation.
Clear communication skills with chemists and biologists.

Nice To Haves

Publications, open-source contributions, or competition results in molecular machine learning.
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.

Responsibilities

Train and maintain machine-learning models for ADMET and developability endpoints, including solubility, permeability, metabolic stability, toxicity, and aggregation risk.
Build potency and binding models, including QSAR and ligand- or structure-based approaches, to rank and prioritize molecules against targets.
Develop synthesizability models, including synthetic-accessibility scoring, reaction-outcome prediction, and retrosynthesis-informed filters.
Apply and fine-tune structure-aware models, including graph neural networks, equivariant networks, protein-ligand structure methods, and docking-informed machine learning.
Curate and clean assay data; define scaffold- or time-split benchmarks; and quantify uncertainty and applicability domains.
Package models for the platform in collaboration with engineers, ensuring they are versioned, reproducible, and monitored for drift.
Present model results and limitations to chemists and project teams, and incorporate their feedback into retraining.