ML Scientist, Foundation Models for Life Sciences
San Francisco, CA$176k - $304kFull-timePosted 5mo ago
About the role
Scientist I or II focused on structure prediction and co-folding within Lila’s Foundation Models team, contributing to problem formulation, model design, training, evaluation, and integration into the Lab-in-the-Loop discovery engine for protein complexes, interactions, and related biomolecular systems in life sciences.
Responsibilities
- Train and evaluate structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems
- Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods
- Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems
- Scale training, inference, and evaluation workflows across large GPU clusters
- Be part of the end-to-end ML process within Lila's “Lab-in-the-Loop” lifecycle: shape data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
- Contribute to adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning
- Translate biological questions into well-defined ML problems and interpret model outputs alongside wet-lab scientists, structural biologists, and computational biologists
- Support research quality and methodology standards within the foundation models program
Qualifications
- PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field (or Master's with equivalent research experience)
- Hands-on experience training deep learning models on molecular, protein, or structural data
- Strong foundation in generative model architectures and training, with demonstrated ability to design careful experiments, ablations, and evaluations
- Ability to formulate and execute research independently, from problem definition through experimentation
- Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, 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
Skills
Required:Deep learning on molecular/structural dataGenerative model architectures and trainingIndependent research executionLife science domain familiarityCollaborating with experimental scientistsML frameworks and GPU workflows
Benefits
- We offer competitive base compensation with bonus potential and generous early-stage equity
- U.S. Benefits: medical, dental, vision; employer-paid life and disability; flexible time off; holidays; parental leave; educational assistance; commuter benefits; subsidized lunch program
- International Benefits: region-tailored programs; USD salaries applicable to U.S.-based positions; international salaries set to local market
Last updated Sep 16, 2026. Interested? Apply on the company site.
About Lila Sciences
Pioneering Scientific Superintelligence
Pioneering Scientific Superintelligence to solve humankind’s greatest challenges.
Scientific SuperintelligenceAIAutonomous LabsLife SciencesChemical SciencesMaterials Sciences
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