Your Impact at LILA

Lila is embarking on a transformative mission to redefine the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we don't just use AI to analyze biology; we are building the loop where AI and automation co-evolve to solve the hardest problems in medicine.

To this end, the Life Science AI team is developing machine learning systems that can reason over biological data and help design better biomolecules. We are seeking an ML Scientist Co-Op to contribute to protein engineering research, including problems related to generative protein design, antibody engineering, developability, and wet-lab-informed model iteration.

This is an opportunity to work alongside Lila scientists on applied ML research at the interface of AI and biology. You will help explore models, datasets, and workflows that connect computational protein design ideas to real experimental needs, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

• Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.
• Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.
• Work with scientists and ML researchers to translate biological design goals into tractable computational problems.
• Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.
• Prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback.
• Communicate results clearly through code, notebooks, written summaries, and presentations to scientific and technical collaborators.

What You'll Need to Succeed

• Currently enrolled as a PhD student in Computer Science, Machine Learning, Computational Biology, Bioengineering, Biophysics, or a related quantitative field.
• Research experience in machine learning, computational biology, protein engineering, or a closely related area.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
• Ability to work with biological sequence, structure, assay, or other scientific datasets.
• Interest in applying ML methods to real biological design problems in partnership with experimental scientists.
• Clear communication skills and comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

• Experience with protein language models, structure prediction, generative protein design, diffusion or flow-based models, or antibody design.
• Familiarity with protein structure, biophysics, developability, affinity maturation, or wet-lab validation concepts.
• Publications, preprints, open-source work, or research projects in ML for biology, protein engineering, or AI for Science.
• Experience building active learning, model evaluation, or data analysis workflows for scientific discovery.
• Comfort collaborating with experimental scientists and translating between ML concepts and biological constraints.