Job description

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) Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $176,000 — $304,000 USD 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.

Requirements

Must-haves

Your Impact at Lila
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
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

Nice-to-haves

Bonus Points For

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aimlmachine learning

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Dental

Vision

Parental Leave