AI/ML Engineer, Peptide Properties and Binding ML
HybridWaltham, Massachusetts, United States
Job Summary
Design and train machine learning models for peptide structure, binding, sequence-function relationships, and property prediction. Build practical modeling workflows to prioritize new peptide designs for synthesis and experimental validation. Apply structural bioinformatics, biophysical modeling, and deep learning methods to understand peptide-target interactions. Evaluate model performance using rigorous prospective and retrospective validation. Work with assay analytics and experimental teams to convert model predictions into testable design hypotheses. Help establish at least one property-modeling focus area, such as solubility, half-life, or another program-relevant peptide property. Produce reproducible modeling workflows, well-documented datasets, and clear technical summaries for project and leadership decisions. This role reports to the Chief Data Science Officer in Waltham, Massachusetts, within a matrix environment of experimental scientists and platform engineers.
Required Qualifications
- AI/ML Engineer, Computational Scientist, or Principal Scientist with a PhD, or MSc with substantial industry experience, in Computational Chemistry, Biophysics, Computer Science, Computational Biology or a related discipline
- Strong programming skills in Python
- hands-on experience with modern ML frameworks such as PyTorch, JAX, TensorFlow or related tools
- Deep expertise in protein, peptide, molecular or structural modeling
- direct experience in binding prediction or sequence-structure-function modeling
- Experience with biophysical modeling, structural bioinformatics, molecular simulation, geometric deep learning, protein language models or related approaches
- Demonstrated ability to evaluate model quality in a scientific setting, including validation strategy, uncertainty, bias and prospective performance
- Strong understanding of how experimental data quality, assay design and synthesis constraints affect ML model usefulness
- Experience working in matrixed teams of experimental and computational scientists to meet project objectives
- Clear communication style, strong organizational skills and the ability to explain modeling decisions to non-specialist scientific stakeholders
- Experience with peptide or protein sequence representations, embeddings, structure-derived features or featurization strategies for ML
- The position is full time
- based at the company's headquarters in Waltham, Massachusetts, USA
- Flexibility with regard to working hours is required
Desired Qualifications
- Experience with peptide therapeutics, constrained peptides, macrocycles, non-natural amino acids or synthetic peptide design
- Experience modeling peptide and miniprotein developability-relevant properties such as proteolytic stability, solubility, half-life or aggregation risk
- Familiarity with active learning, Bayesian optimization, uncertainty estimation or other methods for iterative design cycles
- Experience using public protein structure or interaction resources, such as AlphaFold, PDB, UniProt, ChEMBL or related datasets
- Ability to benchmark emerging protein foundation models and adapt them to sparse, proprietary peptide datasets
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