Research Scientist I/II, In Silico Materials Discovery

Lila Sciences•Cambridge, MA

41d•$176,000 - $234,000

About The Position

Your role in our Physical Sciences division will center on developing the next generation of in silico materials discovery methods, from creating autonomous workflows and data-driven pipelines to building the interface between simulation and AI. You’ll pioneer strategies that enable agents to reason over simulation data, extract latent insights, and guide hypothesis generation and materials design. Your work will expand how we leverage simulation outputs for discovery, accelerating the integration of physics-based modeling and AI reasoning systems. You’ll collaborate with experts in areas spanning simulation, AI agents, and experimental automation to push the boundaries of digital discovery. What You'll Be Building 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 frameworks that utilize simulation data more effectively for prediction, inference, and discovery, including automatic feature extraction, model training, and data-driven exploration. Prototype and evaluate new paradigms for simulation-aware agents that can learn from and act on scientific simulations. Design data representations, metadata standards, and APIs that enable seamless flow of information between simulations, machine learning models, and experimental databases. Create scalable, modular workflows that bridge electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation. Collaborate with computational scientists, machine learning experts, and platform engineers to integrate in silico discovery pipelines into Lila’s broader scientific superintelligence ecosystem.

Requirements

PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related disciplines.
Strong foundation in in silico materials discovery, computational materials modeling, and/or simulation workflow design.
Familiarity with large language models and their application in scientific domains, and
Experience building AI-driven or agentic workflows for scientific automation and discovery.
Solid programming skills in Python and scientific computing frameworks
Familiarity with atomistic simulation software and libraries (e.g., VASP, LAMMPS, ASE, Pymatgen, etc.).

Nice To Haves

Strong publication record in in silico materials discovery, simulation-AI integration, AI-driven inverse design.
Familiarity with scientific agent architectures, large-scale reasoning systems, or multi-agent frameworks for hypothesis generation and experimental planning.
Familiarity with ontologies, metadata standards, and data infrastructure for scientific simulations.
Experience with automated experiment–simulation loops, integrating computational predictions with robotic or cloud-based laboratory platforms.

Responsibilities

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 frameworks that utilize simulation data more effectively for prediction, inference, and discovery, including automatic feature extraction, model training, and data-driven exploration.
Prototype and evaluate new paradigms for simulation-aware agents that can learn from and act on scientific simulations.
Design data representations, metadata standards, and APIs that enable seamless flow of information between simulations, machine learning models, and experimental databases.
Create scalable, modular workflows that bridge electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation.
Collaborate with computational scientists, machine learning experts, and platform engineers to integrate in silico discovery pipelines into Lila’s broader scientific superintelligence ecosystem.