LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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.
- 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...
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