Your Impact at LILA

This PhD-level co-op will join the Life Science AI team to contribute to applied ML research in protein engineering, including generative protein design, antibody engineering, developability, and wet-lab-informed model iteration. Working alongside Lila scientists and ML researchers, the co-op will explore generative and predictive models for protein sequence, structure, and function, analyze biological and experimental datasets to evaluate model outputs, and prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback. Results will be communicated through code, notebooks, written summaries, and presentations.

The ideal candidate is a currently enrolled PhD student in CS, ML, computational biology, bioengineering, biophysics, or a related field, with research experience in ML or protein engineering, strong Python skills, and experience with frameworks like PyTorch or JAX. Familiarity with protein language models, diffusion or flow-based models, antibody design, or active learning workflows is a plus.

The role advances our domain-specific protein design models while the data, methods, and evaluation tools built along the way also strengthen our general AI research model, directly supporting Lila’s mission of combining automated data generation with scientific superintelligence.

What You'll Be Building

• Research generative and predictive models for protein and antibody design
• Analyze experimental datasets to evaluate model outputs and guide design decisions
• Prototype workflows linking model predictions, candidate prioritization, and wet-lab feedback
• Build datasets and benchmarks that support both domain-specific and general AI research models
• Share results through code, notebooks, and presentations

What You'll Need to Succeed

• Currently enrolled PhD student in CS, ML, computational biology, bioengineering, biophysics, or a related field
• Research experience in machine learning, computational biology, or protein engineering
• Strong Python skills and experience with PyTorch, JAX, or similar frameworks
• Experience working with biological sequence, structure, or assay data
• Clear communication and comfort collaborating with cross-disciplinary teams

Bonus Points For

• Experience with protein language models, structure prediction, or generative design (diffusion or flow-based models)
• Familiarity with antibody design, developability, or wet-lab validation concepts
• Publications, preprints, or open-source work in ML for biology