Senior Machine Learning Scientist (AI)

Location: Watertown, MA, USA

Salary: $132,000–$258,500 per year

Company

hirify.global is an early-stage biotechnology company developing Synteins™, a class of macromolecular medicines enabled by synthetic and computational technologies, within the Flagship Pioneering ecosystem.

What you will do

• Train and maintain predictive ML models for ADMET and developability endpoints, including solubility, permeability, metabolic stability, toxicity, and aggregation risk.
• Build QSAR, ligand-based, and structure-based potency and binding models to rank molecules against therapeutic targets.
• Develop synthesizability models for synthetic accessibility, reaction-outcome prediction, and retrosynthesis-informed filtering.
• Apply structure-aware models, including GNNs, equivariant networks, protein-ligand structure modeling, and docking-informed ML.
• Curate assay data, define scaffold or temporal split benchmarks, quantify uncertainty and applicability domains, and communicate model limitations to chemists and project teams.
• Package models with platform engineers for versioned, reproducible deployment and drift monitoring, incorporating project-team feedback into retraining.

Requirements

• PhD in computational chemistry, cheminformatics, machine learning, or a related field, or an MS with equivalent industry experience and approximately 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, and protein or ligand structure.
• Fluency with molecular representations, including fingerprints, descriptors, graphs, and 3D structures, plus tools such as RDKit.
• Strong Python skills and experience with a deep learning framework; PyTorch is preferred. Experience with molecular GNNs or transformers is required.
• Rigorous evaluation practices covering scaffold or temporal splits, baselines, noisy-label handling, uncertainty estimation, and applicability domains.
• 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 with biologics or non-small-molecule modalities.

Culture & Benefits

• Work in an early-stage biotechnology environment focused on therapeutic modality innovation.
• Collaborate with chemists, biologists, platform engineers, and project teams.
• Healthcare coverage, an annual incentive program, retirement benefits, and additional benefits are offered.
• Equal employment opportunity and commitment to diverse and inclusive teams.

Hiring process

• Applications are processed through Greenhouse and may be reviewed using AI-assisted screening tools.
• Unsolicited resumes from recruitment or staffing agencies are not accepted unless the agency was contacted directly by Flagship Pioneering's internal Talent Acquisition team.