About the role
Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve hard problems across scientific domains. Within Life Sciences AI, we are developing autonomous-science capabilities for biological systems, spanning multiple biological domains and resolutions, based on multi-modal data and foundation models. We are seeking a Co-Op, LS AI, ML Scientist for Biology to contribute to cutting-edge research on how to effectively evaluate, guide, and reinforce agentic model behavior in this domain. This is an opportunity to work alongside Lila scientists on early-stage research in autonomous life science AI. You will help explore reasoning models, evaluation and benchmark datasets, and workflows that connect modern AI methods to real biological questions, gaining hands-on experience in a fast-moving scientific environment. What You'll Be Building Contribute to ML research on reasoning models for biological discovery and autonomous science. Explore methods to evaluate, guide, and reinforce agentic model behavior in biological domains. Help develop evaluation and benchmark datasets for biological reasoning tasks. Analyze multi-modal biological data to identify useful signals for model evaluation and improvement. Prototype workflows that connect model reasoning, evaluation, and scientific feedback. Communicate findings through code, notebooks, written summaries, and presentations. What You'll Need to Succeed Currently enrolled in a PhD program in Computer Science, Machine Learning, Computational Biology, Bioengineering, or a related quantitative field. Research experience in machine learning, AI for science, computational biology, or biological data analysis. Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools. Experience working with biological, scientific, or multi-modal datasets. Interest in reasoning models, agentic systems, evaluation methods, or benchmark design. Interest in closed-loop scientific discovery, autonomous labs, or AI systems that interact with experimental feedback. Ability to communicate research findings clearly through code, notebooks, written summaries, and presentations. Comfort working in a collaborative, cross-disciplinary research environment.
