ML Scientist I / II, Foundation Models for Life Sciences

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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 researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains. We are seeking a Scientist I or II to join this team as a contributor to foundation model research at the intersection of machine learning and life science data. You will work on generative models spanning biological sequences, molecular structures, and multimodal experimental data, contributing to 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 generative AI applied to 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 Contribute to research on foundation models for life science applications, including biological sequence design, structure prediction, and multimodal scientific reasoning Design, train, and evaluate generative models on biological and chemical data, incorporating domain-specific constraints and priors Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: support data generation strategy, build pipeline models, and help design feedback loops where experimental results improve model performance Translate biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists Support research quality and methodology standards within the foundation models program What You'll Need to Succeed PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field (or Master's with equivalent research experience) Strong foundation in generative model architectures and training, with hands-on experience in model development and evaluation Ability to formulate and execute research independently, from problem definition through experimentation Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related) Experience collaborating with experimental scientists or working with biological/chemical data Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows Bonus Points For Experience in computational protein design or molecular structure prediction Experience with active learning loops or closed-loop experimental workflows Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications Familiarity with distributed training infrastructure High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)

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