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Salary: USD 176,000 - 304,000 / annual
Lila Sciences is building an autonomous science platform that combines advanced AI models with proprietary instruments to accelerate discovery across medicine, materials, and energy. The AI for Protein Engineering team develops and applies generative and predictive models to move biomolecule design programs from computational hypothesis to wet-lab validated leads.
You will work at the intersection of machine learning, protein engineering, and therapeutic design. Your responsibilities include:
• Building ML workflows for protein engineering campaigns, spanning design specification through experimental learning cycles.
• Developing and adapting methods for de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning.
• Integrating protein design methods into robust software systems and broader reasoning models.
• Translating therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans.
• Partnering with experimental scientists to interpret why designed biomolecules succeed or fail, then turning those insights into model improvements.
• Building evaluation frameworks for model generalization to challenging biologics design problems.
You will collaborate with experimental scientists, AI researchers, and platform teams to support Lila's broader autonomous science platform.
QUALIFICATIONS & REQUIREMENTS:
Required:
• 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.
Bonus:
• 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.
