Research Scientist I/II, In Silico Materials Discovery

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.

Introduction

Lila Sciences is seeking a Research Scientist I/II to join their Physical Sciences division. This role focuses on developing next-generation in silico materials discovery methods, integrating physics-based modeling and AI reasoning systems to accelerate digital discovery.

Responsibilities

• Develop methods and workflows for in silico materials discovery connecting physics-based simulations, generative models, and agentic AI systems.
• Build intelligent pipelines for AI agents to autonomously design, launch, interpret, and refine simulations.
• Design frameworks for effective utilization of simulation data for prediction, inference, and discovery, including automatic feature extraction, model training, and data-driven exploration.
• Prototype and evaluate new paradigms for simulation-aware agents capable of learning from and acting on scientific simulations.
• Design data representations, metadata standards, and APIs for seamless information flow between simulations, machine learning models, and experimental databases.
• Create scalable, modular workflows bridging 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 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.
• 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 Have

• 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.

Benefits

• Competitive compensation including bonus potential and generous early equity. The final offer will reflect your unique background, expertise, and impact.

About Company

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai.

How to Apply

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed, you are encouraged to apply. 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 their Candidate Privacy Policy.