AI Scientist I/II, Generative Modeling for Materials Science

Your Impact at Lila As a Research Scientist in our Physical Sciences organization, you develop state-of-the-art generative modeling techniques applied to critical challenges in materials science. You will be working with cross-functional machine learning experts, software engineerings and materials scientists across multiple teams at Lila to create and deploy generative models for Lila’s unique materials design challenges. In addition to pushing the state of the art, what is really exciting about this role is to see a clear impact of your methods on real-world materials that are being built and improved in our experimental facilities on a daily basis. What You'll Be Building Generative Models: Design and implement generative models (Diffusion models, flow-based models) and advanced sampling methods for diverse materials design challenges. Data Representation: Develop novel architectures methods based on physics-informed constraints and domain knowledge informed inductive biases aimed at representing and modeling materials across a wide range of chemical space for real-world applications. Real-World Validation Deployment: Create and validate datasets, frameworks, and methods for validating generative models on experimentally realized materials. Partner with software engineers and product managers to deploy solutions. Cross-Functional Partnership: Work closely with RD leadership, product managers, and automation specialists to translate scientific questions into data requirements and modeling strategies. What You’ll Need to Succeed Proficiency in Python, deep learning frameworks and end-to-end workflow deployment. Understanding of modern generative modeling methods (diffusion models, flow matching models, geometric deep learning methods) and their applications to scientific problems, including materials science, chemistry or biology (e.g. proteins). Elementary understanding of materials science, physics and chemistry and how their principles can be infused into generative model design. Strong self-starter and independent thinker, with strong attention to detail. Demonstrated industry experience or academic achievement. Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner. Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting. Bonus Points For PhD in Materials Science, Computer Science, Physics, Chemistry, or related field with strong publication record in machine learning (NeurIPS, ICML, ICLR) and scientific (Nature, Science, Cell Press Matter, IOP) venues. Experience with computational materials science methods (DFT, Molecular Dynamics). Understanding of experimental materials science techniques related to synthesis and characterization. About Lila Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, 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 c ... (truncated, view full listing at source)