Co-op, Ls Ai, ML Scientist for Protein Engineering

Lila Sciences Lila Sciences · AI Frontier · San Francisco, CA · AI

ML Scientist Co-Op role focused on protein engineering research, including generative protein design and antibody engineering. The role involves exploring generative and predictive modeling approaches for biomolecules, analyzing biological datasets, and prototyping workflows that connect model predictions with wet-lab feedback. This is an applied ML research position at the intersection of AI and biology.

What you'd actually do

  1. Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.
  2. Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.
  3. Work with scientists and ML researchers to translate biological design goals into tractable computational problems.
  4. Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.
  5. Prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback.

Skills

Required

  • 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
  • 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
  • comfort working in a collaborative, cross-disciplinary research environment.

Nice to have

  • 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.

What the JD emphasized

  • protein engineering research
  • generative protein design
  • antibody engineering
  • wet-lab-informed model iteration
  • ML research projects
  • generative and predictive modeling approaches
  • biological design goals
  • biological and experimental datasets
  • model predictions
  • wet-lab feedback

Other signals

  • ML systems that reason over biological data
  • generative protein design
  • antibody engineering
  • wet-lab-informed model iteration