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

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.