Co-Op, Agentic Simulations for Materials Science

The role

You will develop ML and agent-driven infrastructure to enable scientific superintelligence to construct, execute, and interpret physics simulations. This work applies to real-world problems in materials science and chemistry.

What you will do

• Contribute to projects involving AI agents for simulations, ML models for atomistic simulations, and in silico materials discovery.
• Develop methods and workflows for in silico materials discovery connecting physics-based simulations, generative models, and agentic AI systems.
• Build intelligent pipelines where AI agents 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 discovery pipelines.

Required qualifications

• 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

Nice to have

• Prior experience and publications in 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

About the company

Lila Sciences is building Scientific Superintelligence to solve humankind's greatest challenges. The company builds systems that can learn for themselves by combining advanced AI models with proprietary AI Science Factory instruments.