Senior ML Scientist, AI for Protein Engineering
Core
Develop and apply generative and predictive ML models to move biomolecule design from in silico hypothesis to wet-lab validated leads.
Role type
Senior individual contributor machine learning scientist (protein engineering)
Builds
Applied ML workflows for protein engineering campaigns, integrating methods into robust software systems and broader reasoning models.
Domain
Life Sciences / Computational Biology / Protein Engineering
Deliverable
production ML models
Required skills
Deep ML expertise, hands-on adaptation of modern AI methods, intuition for therapeutic biologics design, ability to drive applied research independently, collaboration across ML/biology/software teams
Preferred skills
Experience designing antibodies/nanobodies/enzymes/peptides, structure prediction, generative protein design, diffusion models, flow matching, protein language models, structural biology, conformational dynamics, closing design-test-learn loops with wet-lab teams
Technologies
diffusion models, flow matching, protein language models
Responsibilities
Own applied ML workflows for protein engineering campaigns from design specification through experimental learning; Develop and adapt methods spanning de novo generation, property prediction, candidate selection, and active learning; Translate therapeutic questions into ML problems and evaluation plans; Partner with experimental scientists to interpret model outputs and improve design principles; Build rigorous evaluation frameworks for model generalization
Seniority
Senior, hands-on IC