Co-Op, LS AI, ML Scientist for Protein Engineering

About the role#

Lila Sciences is seeking an ML Scientist Co-Op to join the Life Science AI team. This role focuses on applied machine learning research at the intersection of AI and biology, specifically targeting protein engineering, antibody design, and biomolecule development. You will work alongside scientists and ML researchers to bridge the gap between computational design and experimental feedback.

What you'll do#

• 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.
• Translate biological design goals into tractable computational problems in collaboration with scientists and ML researchers.
• 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 through code, notebooks, and presentations to technical collaborators.

What you'll need#

• 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 or JAX.
• 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.
• Bonus qualifications include experience with protein language models, structure prediction, generative design, or diffusion models, as well as familiarity with biophysics or wet-lab validation concepts.

Location & details#

• Location: San Francisco, California
• Modality: On-site
• Employment: Full-time, paid internship
• Term: Rolling