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Tier-1 employer, metro location, and sought-after senior ML role create moderate applicant competition.
Protein engineering and drug-discovery ML specialization makes skills less transferable across industries.
Explicit senior years requirements plus domain-specific ML, biotech expertise, and publication expectations make screening stringent.
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Develop and apply predictive machine learning models for biologics properties including developability and clinical immunogenicity endpoints.
Train, fine-tune, and evaluate foundation models for sequence/structure-to-property prediction and biologics design using multimodal datasets.
Design active learning and Bayesian optimization strategies to prioritize experiments and support biologics discovery decisions.
Doctorate degree with 4+ years in Data Science, Computational Biology, or related field, OR Master's degree with 8+ years of directly related experience.
Strong proficiency in Python and experience with deep learning frameworks such as PyTorch.
Experience integrating diverse biological data (sequence, structure, imaging, assay) into predictive modeling workflows.
Work Experience Required: 4+ years post-PhD or 8+ years with Master's.
Experienced in machine learning applications for biologics, protein engineering, drug discovery, or immunology domains.
Proficient in advanced modeling techniques including foundation models, representation learning, active learning, and uncertainty quantification.
Skilled in reproducible ML engineering practices and communicating scientific results to cross-functional teams and in high-impact publications.