Co-op, AI for Protein Design

Lila Sciences • San Francisco, CA

4d

About The Position

This PhD-level co-op will join Lila Sciences’ Life Science AI team to contribute to applied machine learning 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 connecting model predictions, candidate prioritization, and wet-lab feedback. Results will be communicated through code, notebooks, written summaries, and presentations.

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

Lila Sciences is building Scientific Superintelligence to address major challenges through AI-driven science. It combines advanced AI models with proprietary AI Science Factory instruments in an operating system designed to execute the scientific method autonomously and accelerate discovery across medicine, materials, and energy. The company emphasizes truth, trust, curiosity, grit, and velocity, and is committed to equal employment opportunity.

Requirements

• Currently enrolled PhD student in computer science, machine learning, 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 skills and comfort collaborating with cross-disciplinary teams.

Nice To Haves

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

Responsibilities

• 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 domain-specific and general AI research models.
• Communicate results through code, notebooks, written summaries, and presentations.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree