Lila Sciences is a startup building an AI platform that trains models on scientific literature and integrates laboratory validation to enable autonomous scientific discovery.
<p><strong>Your Impact at Lila</strong></p> <p>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 researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains.</p> <p>We are seeking a Scientist I or II to join this team as a contributor to foundation model research at the intersection of machine learning and life science data. You will work on generative models spanning biological sequences, molecular structures, and multimodal experimental data, contributing to problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.</p> <p>This is an IC role for someone building deep expertise in generative AI applied to 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.</p> <p><strong>What You'll Be Building</strong></p> <ul> <li>Contribute to research on foundation models for life science applications, including biological sequence design, structure prediction, and multimodal scientific reasoning</li> <li>Design, train, and evaluate generative models on biological and chemical data, incorporating domain-specific constraints and priors</li> <li>Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: support data generation strategy, build pipeline models, and help design feedback loops where experimental results improve model performance</li> <li>Translate biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists</li> <li>Support research quality and methodology standards within the foundation models program</li> </ul> <p><strong>What You’ll Need to Succeed</strong></p> <ul> <li>PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field (or Master's with equivalent research experience)</li> <li>Strong foundation in generative model architectures and training, with hands-on experience in model development and evaluation</li> <li>Ability to formulate and execute research independently, from problem definition through experimentation</li> <li>Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related)</li> <li>Experience collaborating with experimental scientists or working with biological/chemical data</li> <li>Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows</li> </ul> <p><strong>Bonus Points For</strong></p> <ul> <li>Experience in computational protein design or molecular structure prediction</li> <li>Experience with active learning loops or closed-loop experimental workflows</li> <li>Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications</li> <li>Familiarity with distributed training infrastructure</li> <li>High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)</li> </ul> <p> </p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><strong>Compensation</strong></p> <p>We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.</p> <p><strong>U.S. Benefits.</strong> 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.</p> <p><strong>International Benefits.</strong> 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.</p></div><div class="title">Expected Base Salary Range</div><div class="pay-range"><span>$176,000</span><span class="divider">—</span><span>$304,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>About LILA</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>We’re All In</strong></p> <p>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.</p> <p>Information you provide during your application process will be handled in accordance with our <a href="https://www.lila.ai/candidate-privacy-policy-notice">Candidate Privacy Policy</a>.</p> <p><strong>A N