Intern - Physical Sciences

Spring 2026 Co-op/Internship OpportunityWe’re looking for motivated students to join our team starting in January/February 2026 for a spring co-op or internship. This is a great opportunity to gain hands-on experience, contribute to meaningful projects, and develop your skills in a fast-paced, collaborative environment. If you’re eager to learn and make an impact, we’d love to hear from you!

Company Summary

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

Lila is seeking a collaborative and thoughtful Co-op Intern to join our Physical Sciences team in Cambridge, MA. In this role, you will support the development of high-throughput X-ray diffraction (XRD) analysis workflows. You’ll focus on coding, data reduction, and integration of diffraction datasets with state-of-the-art machine learning algorithms. By working closely with our Platform Science, Characterization, and ML teams, you’ll gain experience designing reproducible workflows in a multidisciplinary environment at the cutting edge of materials discovery.

What You'll Be Building

• XRD Analysis: Deploy methods for post-processing of diffraction data, including azimuthal integration, peak analysis, and phase identification.
• Data Pipelines: Contribute to coding workflows for scalable data reduction and visualization.
• ML Integration: Work with ML researchers to connect processed diffraction features to predictive models.
• Collaborative Workflows: Partner with scientists and engineers to design efficient, reproducible analysis processes.
• Code Practices: Apply clean coding, version control, and documentation standards to support team-wide reproducibility. 

What You’ll Need to Succeed

• Current enrollment in a Master’s or Ph.D. program in Physics, Computer Science, Materials Science & Engineering, or a related field.
• Strong proficiency in Python (NumPy, pandas, scikit-learn); SQL familiarity a plus.
• Research or project experience involving data analysis and scientific programming.
• Fundamental knowledge of X-ray diffraction principles and analysis (azimuthal integration, phase identification, scattering physics). 

Bonus Points For

• Experience with Julia or numerical optimization libraries.
• Familiarity with XRD software and libraries (pymatgen, pyFAI, VESTA, Jade, etc.).
• Exposure to machine learning methods, Bayesian optimization, or workflow automation.