lilasciences · San Francisco, CA USA
Your Impact at Lila Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains. We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein–protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine. This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational–experimental loop, and contribute to Lila's presence in the broader scientific community. What You'll Be Building Train and evaluate structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems Scale training, inference, and evaluation workflows across large GPU clusters Be part of the end-to-end ML process within Lila's “Lab-in-the-Loop” lifecycle: shape data generation strategy, build pipeline models, and design …truncated… ence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI m…
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