LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
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.
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
More about this role
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.
- Build ML workflows for protein engineering campaigns, from design specification through experimental learning.
- Develop and adapt methods spanning de novo generation, sequence- or...
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