AI Scientist I/II, Generative Modeling for Materials Science
As a Research Scientist in the Physical Sciences organization at Lila Sciences, you will develop state-of-the-art generative modeling techniques to address critical challenges in materials science. Collaborating with cross-functional teams of machine learning experts, software engineers, and materials scientists, you will create and deploy generative models tailored to Lila's unique materials design needs. This role offers the opportunity to see the tangible impact of your methods on real-world materials being developed and enhanced in our experimental facilities daily.
Key responsibilities include designing and implementing generative models, such as diffusion and flow-based models, and advanced sampling methods for diverse materials design challenges. You will develop novel architectures and methods based on physics-informed constraints and domain-specific inductive biases to represent and model materials across a wide chemical space for practical applications. Additionally, you will create and validate datasets, frameworks, and methods for assessing generative models on experimentally realized materials, partnering with software engineers and product managers to deploy these solutions. Collaborating closely with R&D leadership, product managers, and automation specialists, you will translate scientific questions into data requirements and modeling strategies.
To succeed in this role, you should have proficiency in Python, deep learning frameworks, and end-to-end workflow deployment. A solid understanding of modern generative modeling methods, including diffusion models, flow matching models, and geometric deep learning methods, and their applications to scientific problems in materials science, chemistry, or biology is essential. An elementary understanding of materials science, physics, and chemistry, and how their principles can be integrated into generative model design, is also required. Strong self-starting and independent thinking abilities, attention to detail, demonstrated industry experience or academic achievement, and excellent communication and presentation skills are necessary. A willingness to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting is important.
Preferred qualifications include a PhD in Materials Science, Computer Science, Physics, Chemistry, or a related field with a strong publication record in machine learning and scientific venues. Experience with computational materials science methods, such as Density Functional Theory (DFT) and Molecular Dynamics, and an understanding of experimental materials science techniques related to synthesis and characterization are advantageous.
Lila Sciences offers competitive base compensation with bonus potential and generous early-stage equity. 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. The expected base salary range for this role is $176,000 to $304,000 USD.