ML Engineer, LS AI

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 ML Engineer 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)

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.