AI Residency Program, Material Science (2026 Cohort)
About The Position
The AI Residency Program is a full-time research opportunity designed to bridge the gap between academic research and industry applications in AI for materials science. Residents will work closely with Lila scientists and engineers on high-impact, open-science projects, with the option to focus on either fundamental or applied research. Duration: 6–12 months (extension possible) Start Dates: First hires beginning January 2026, with rolling applications and additional intakes in Summer and Fall 2026 Cohort Size: Small group of selected residents Mentorship: Pairing with technical mentors, feedback from cross-functional teams Resources: Access to proprietary datasets, high-performance compute, and Lila’s research infrastructure Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation. The Lila Sciences AI Residency is a full-time research program at the intersection of artificial intelligence and materials science. As a resident, you'll join a cohort of researchers tackling open-ended scientific challenges alongside Lila’s world-class team of scientists and engineers. With access to proprietary datasets, high-performance compute infrastructure, and experienced mentors, you'll pursue ambitious research projects with both academic and real-world impact. Publishing is encouraged but not required — what matters most is pushing the frontier of scientific discovery.
Requirements
- Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor’s, Master’s, or PhD)
- Proficiency in Python and deep learning frameworks (e.g., PyTorch)
- Experience working with large-scale datasets or simulations
- Familiarity with modern AI/ML architectures and training techniques
- Strong research background, demonstrated through publications, thesis work, or open-source projects
Nice To Haves
- Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
- Familiarity with Bayesian optimization, active learning, or generative models
- Experience in reinforcement learning or agent-based approaches to scientific reasoning
- Open-source contributions or collaborative research experience
- Strong communication and writing skills, especially for conveying complex scientific ideas
Responsibilities
- Design and execute independent research projects in AI for materials science
- Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
- Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
- Contribute to collaborative team research and co-develop novel approaches to scientific discovery
- Share findings internally and externally; publications are welcome but not mandatory
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Number of Employees
101-250 employees
