Co-Op, ML Scientist for Biology
About The Position
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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- Startup speed while tackling problems of historic importance.
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What This Job Offers
Career Level
Entry Level
Education Level
Ph.D. or professional degree
