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Research Scientist I/II in In Silico Materials Discovery

Lila SciencesCambridge, MA USA
On-site Full-time $176K/yr - $234K/yr

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Experience Level

Mid to Senior

Qualifications

Qualifications for Success PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related fields. Strong background in in silico materials discovery, computational materials modeling, and/or simulation workflow design. Familiarity with large language models and their applications in scientific research. Experience in developing AI-driven or agentic workflows for scientific automation and discovery. Proficient programming skills in Python and scientific computing libraries.

About the job

Your Role at Lila Sciences

As a key contributor to our Physical Sciences division, you will focus on advancing the field of in silico materials discovery techniques. Your responsibilities will involve designing autonomous workflows and data-driven pipelines that effectively integrate simulation with artificial intelligence. You will lead the development of strategies that empower agents to analyze simulation data, derive hidden insights, and facilitate hypothesis generation and materials design. Your innovative approach will enhance our utilization of simulation outputs for discovery, fostering a seamless integration of physics-based modeling with AI reasoning systems. Collaboration with specialists in simulation, AI agents, and experimental automation will be pivotal as we strive to redefine the landscape of digital discovery.

Key Contributions

  • Create cutting-edge workflows for in silico materials discovery that link physics-based simulations and generative AI models.
  • Develop intelligent systems where AI agents autonomously design, execute, interpret, and optimize simulations.
  • Establish frameworks that maximize the utility of simulation data for prediction, inference, and discovery, with features like automatic data extraction and model training.
  • Prototype and assess novel paradigms for simulation-aware agents that learn from and interact with scientific simulations.
  • Design data structures, metadata specifications, and APIs to ensure smooth information transfer among simulations, machine learning models, and experimental data repositories.
  • Build scalable, modular workflows that connect electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation.
  • Work alongside computational scientists, machine learning professionals, and platform engineers to integrate in silico discovery pipelines into Lila’s overarching scientific superintelligence framework.

Qualifications for Success

  • PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related fields.
  • Strong background in in silico materials discovery, computational materials modeling, and/or simulation workflow design.
  • Familiarity with large language models and their applications in scientific research.
  • Experience in developing AI-driven or agentic workflows for scientific automation and discovery.
  • Proficient programming skills in Python and scientific computing libraries.

About Lila Sciences

At Lila Sciences, we are committed to pioneering advancements in scientific discovery through the innovative application of artificial intelligence and simulation technologies. Our team of experts collaborates across disciplines to push the boundaries of materials science and digital discovery. Join us in our mission to transform the future of science.

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