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 Principal or Senior Principal Scientist to join this team as a core contributor. You will define and drive research at the intersection of state-of-the-art machine learning and life science data, spanning biological sequences, molecular structures, and multimodal experimental data. As part of a dynamic team, you will design and implement foundational models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.
This is a high-impact IC role for someone who operates at the frontier of generative AI applied to biology. You will shape the technical agenda for foundation model research, collaborate closely with experimental scientists to close the computational-experimental loop, and represent Lila's work to the broader scientific community.
What You'll Be Building
- Drive research on foundation models for life science applications, including but not limited to biological sequence design, structure prediction, and multimodal scientific reasoning
- Design, train, and evaluate large-scale generative models on biological and chemical data, integrating domain-specific constraints and priors
- Contribute to the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
- Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists
- Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams
- Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement
What You’ll Need to Succeed
- PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field
- Multiple high-impact first-author or senior-author publications at premier venues (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
- Deep expertise in large-scale generative model architectures and training, with hands-on experience training models on distributed infrastructure
- Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment
- Fluency across ML and at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related), with experience designing computational experiments grounded in biological reality
- Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology
- Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)
Bonus Points For
- Experience in computational protein design, particularly antibody and nanobody engineering
- Experience designing biological sequences or molecular structures with demonstrated wet-lab validation
- Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
- Experience with agentic frameworks or active learning loops in scientific contexts
- High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)