ML Scientist I/II, AI for Protein Engineering

Lila Sciences • San Francisco, CA

11d

About The Position

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team develops and applies generative and predictive models that move biomolecule design programs from in silico hypothesis to wet-lab validated leads. We are looking for an ML Scientist I/II focused on AI for protein engineering. The work spans active protein engineering programs and focused technology development that improves how Lila designs, evaluates, and learns from biomolecular sequence, structure, and function data. This role sits at the intersection of machine learning, protein engineering, and therapeutic design. The ideal candidate brings strong ML fundamentals, curiosity about protein biology, and interest in computationally designed, wet-lab-validated biologics. You’ll collaborate with experimental scientists, AI researchers, and platform teams to build models and workflows that support Lila’s broader autonomous science platform.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• competitive base compensation with bonus potential and 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

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree