# (Senior) Machine Learning Scientist

**Company**: Abiologics, Inc.
**Location**: Watertown, MA
**Experience**: senior
**Job type**: full-time
**Salary**: $132,000 - $258,500
**Category**: Data Science
**Industry**: Healthcare

**Apply**: https://job-boards.greenhouse.io/flagshippioneeringinc/jobs/8931439002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/job/senior-machine-learning-scientist-848b7894-d6f/

## Description

Abiologics, Inc. is seeking a Machine Learning Scientist to train, validate, and ship predictive models that power their 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.

Responsibilities:

- 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.

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.

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.

Benefits:

- Healthcare coverage

- Annual incentive program

- Retirement benefits

- Other benefits

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## Skills

### Required
- machine learning
- computational chemistry
- cheminformatics
- Python
- deep learning
- RDKit

### Nice to have
- generative design
- active learning
- Bayesian optimization
- large-scale training
- MLOps
- model serving
- docking
- free-energy methods
- protein structure prediction

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