AI Residency Program, Material Science (2026 Cohort)
Lila SciencesCambridge, MA USAPosted 26 March 2026
Job Description
AI Resident – 2026 Cohort
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.
Application Requirement:
Please submit your resume alongside a research proposal (up to 3 pages, unlimited references) outlining the project you would plan to pursue during your residency at Lila Sciences.
Please submit your research proposal as your cover letter.
Applications without both documents will not be considered. Optional supporting materials (e.g., recommendation letters, publications, research artifacts) may also be included.
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.
Your Impact at Lila
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.
What You'll Be Building
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
What You’ll Need to Succeed
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
Bonus Points For
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 con ... (truncated, view full listing at source)
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