ML · Python
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, candi