Co-Op, Agentic Simulations for Materials Science

Your Impact at LILA

Join our physical sciences team to contribute to developing ML- and agent-driven infrastructure that enables Lila's scientific superintelligence to autonomously construct, execute, and interpret complex physics simulations and apply these to real-world scientific problems in materials science and chemistry. You will work on projects involving one of these areas: enabling AI agents to reason over and orchestrate simulations, developing ML methods to accelerate atomistic simulations, and leveraging agentic simulations for applications in materials discovery.

What You'll Be Building

• Contribute to projects involving one of these areas: AI agents for simulations, ML models for atomistic simulations, and in silico materials discovery.
• Develop methods and workflows for in silico materials discovery that connect physics-based simulations, generative models, and agentic AI systems.
• Build intelligent pipelines where AI agents can design, launch, interpret, and refine simulations autonomously.
• Design new methods to accelerate simulations with ML-based surrogate models.
• Collaborate with computational scientists, machine learning experts, and platform engineers to integrate in silico discovery pipelines into Lila's broader scientific superintelligence ecosystem.

What You'll Need to Succeed

• Currently enrolled in an MS or PhD program in CS, Materials Science, Chemistry, Chemical Engineering, Physics, or a related field (exceptional BS candidates with research experience considered)
• Proficiency in Python, deep learning frameworks and end-to-end workflow deployment
• Evidence of applied ML research work in physical sciences including method development, model training and evaluation, and sound code practices
• Strong 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

Bonus Points For

• Prior experience and publications in one or more of these areas: ML interatomic potential development, LLMs for materials science and chemistry, Generative models for materials, High-throughput computational materials discovery
• Contributions to open-source simulation or ML infrastructure and software
• Familiarity with materials science simulation codes and workflow orchestration frameworks