ML Scientist I/II, AI for Protein Engineering

Lila Sciences is building AI and automation platforms to accelerate scientific discovery across medicine, materials, and energy. The ML Scientist I/II will develop and apply machine learning models and workflows for protein engineering, spanning biomolecular generation, prediction, candidate selection, active learning, and experimental validation. The role collaborates with experimental scientists, AI researchers, and software teams to improve computationally designed biologics.

What you'll do:

• Build ML workflows for protein engineering campaigns, from design specification through experimental learning
• Develop and adapt methods spanning de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning
• Integrate protein design methods into robust software systems and broader reasoning models
• Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans
• Partner with experimental scientists to interpret why designed biomolecules succeed or fail, then turn those insights into model improvements
• Build evaluation frameworks for model generalization to challenging biologics design problems

What they're looking for:

• PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field
• Experience applying machine learning to protein design, biologics engineering, or related biomolecular design problems
• Strong ML fundamentals, with hands-on experience developing, adapting, training, or evaluating modern AI methods
• Fluency with biological sequence, structure, function, developability, or experimental validation considerations
• Ability to translate therapeutic or biological objectives into computational design problems and model evaluation plans
• Strong collaboration and communication skills across ML, biology, experimental science, and software teams
• Experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins
• Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models
• Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or other biophysical constraints
• Experience closing design-test-learn loops with wet-lab teams, including experimental prioritization, high-throughput validation, and active learning
• Industry experience translating ML research into practical biological design workflows, experimental campaigns, or platform capabilities
• Publications, open-source contributions, or applied research outputs in AI for science venues

Benefits:

• Bonus potential
• Generous early-stage equity
• Medical, dental, and vision coverage
• Employer-paid life and disability insurance
• Flexible time off with generous company wide holidays
• Paid parental leave
• An educational assistance program
• Commuter benefits, including bike share memberships for office based employees
• A company subsidized lunch program