ML Scientist I / II, Foundation Models for Life Sciences

About the role

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.

Responsibilities

• 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

Requirements

• 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

Qualifications

• 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)

Skills

• Strong programming skills in Python, R, or similar languages
• Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Knowledge of generative modeling techniques and their applications in biology
• Experience with handling and preprocessing biological and chemical data
• Excellent communication and collaboration skills

Benefits

Compensation: Competitive base compensation with bonus potential and generous early-stage equity.

U.S. Benefits: Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits: Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region.