ML Scientist, Foundation Models for Life Sciences.

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 builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains.

We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein–protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.

This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational–experimental loop, and contribute to Lila's presence in the broader scientific community.

What You'll Be Building

• 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

What You’ll Need to Succeed

• 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

Bonus Points For

• Experience training or extending co-folding, structure prediction, protein–protein, or diffusion deep learning models
• Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models
• Antibody, biologics, or protein design experience, including structure-guided optimization
• Familiarity with distributed training infrastructure and large-scale scientific data pipelines
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Experience with active learning loops or closed-loop experimental workflows
• Experience integrating ML models into agentic scientific workflows
• High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)