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Tier-1 employer and metro location increase competition, but niche protein-ML specialization reduces candidate density.
Highly specialized protein ML and wet-lab collaboration requirements limit transferability across industries.
Explicit senior years, degree alternatives, required domain publications and specialized ML and biotech skills.
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Develop and implement machine learning models to predict sequence-to-property relationships in proteins, improving biomolecular developability and structural tractability.
Create scalable, reproducible workflows for large-scale biological data processing, feature generation, model training, and evaluation.
Collaborate with multidisciplinary teams to support experimental decision-making from early design to structural characterization, applying techniques like active learning and Bayesian optimization.
Doctorate degree with 4+ years experience or Master’s degree with 8+ years experience in Data Science, Computational Biology, Bioinformatics, or related field.
Strong programming skills in Python and experience with ML frameworks such as PyTorch.
Experience developing predictive ML models for biological or biophysical data and working with large-scale datasets, including cloud platforms (e.g., AWS).
Work Experience Required: Minimum 4 years post-PhD or 8 years post-Master’s in relevant fields.
Experienced in advanced ML techniques such as protein language models, generative modeling, and uncertainty-aware approaches targeting protein science applications.
Comfortable operating in a cross-functional environment with experimental teams to integrate computational predictions with laboratory workflows.
Skilled in reproducible ML and software engineering practices including version control, containerization, and experiment tracking for robust and scalable model development.