AI Scientist I/II, Generative Modeling for Materials Science
About The Position
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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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 R&D leadership, product managers, and automation specialists to translate scientific questions into data requirements and modeling strategies.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
101-250 employees
