Co-Op, ML Scientist for Biology

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.

Bonus Points For

• Experience with reasoning models, agentic systems, reinforcement learning, or model evaluation.
• Experience developing benchmarks, evaluation datasets, or model assessment workflows.
• Publications, preprints, talks, posters, or workshop presentations in ML, AI for science, computational biology, or related scientific venues.