Principal, Machine Learning Engineer

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 Senior or Principal Scientist to set the direction for our 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 own models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone operating at the frontier of structure-aware generative AI for biology. You will shape the technical agenda for structural foundation model research, collaborate closely with experimental scientists to close the computational–experimental loop, and represent Lila's work to the broader scientific community.

What You'll Be Building

• Drive research on structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems
• Design, train, and evaluate models that advance the state of the art in AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods
• Set the evaluation bar for the program, building frameworks that establish model generalization to challenging de novo design problems
• Own training, inference, and evaluation at scale across large GPU clusters
• Shape the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
• Extend into adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning
• Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists, structural biologists, and computational biologists
• Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams
• Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement

What You’ll Need to Succeed

• PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field
• Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment
• Fluency across ML and at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related), with experience designing computational experiments grounded in biological reality
• Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology
• Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)

Bonus Points For

• Strong expertise in structure prediction, co-folding, geometric deep learning, or structure-aware molecular ML, with a track record of training these models
• Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models
• Experience in computational protein design, particularly antibody and nanobody engineering
• Strong expertise in generative model architectures and training, with hands-on experience training models on distributed infrastructure
• Experience designing biological sequences or molecular structures with demonstrated wet-lab validation
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Experience with agentic frameworks or active learning loops in scientific contexts
• Multiple high-impact first-author or senior-author publications, or open-source contributions in AI for Science, at premier venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)