AI Research Scientist — Agentic AI for Materials Discovery

Meta•Redmond, WA

About The Position

This role sits within Meta's Reality Labs Research, in the Material and Systems Innovation team, which develops advanced materials for two of Meta's most ambitious hardware frontiers: lightweight, all-day wearable AR glasses and next-generation sensing and actuating materials for robotics. The AI Specialist will design and build LLM-orchestrated multi-agent systems that autonomously drive materials discovery pipelines — from computational screening and simulation through synthesis and characterization — across both domains. By closing this loop with agentic AI, we aim to compress discovery timelines from years to weeks, directly accelerating Meta's ability to ship breakthrough AR/VR and robotics hardware. This position bridges two of Meta's highest-priority investment areas — frontier AI and the physical systems underpinning the metaverse and embodied intelligence — and will contribute both production systems and published research at top-tier venues.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
PhD in AI, Computer Science, Computational Chemistry, Materials Science, or related field
2+ years of experience with large language models, prompt engineering, or agentic AI frameworks (e.g., React, tool-use agents, multi-agent orchestration)
Demonstrated programming skills in Python and experience with ML frameworks (PyTorch, JAX, or similar)
Demonstrated experience in building end-to-end AI systems that integrate external tools and APIs
Familiarity with at least one domain: computational chemistry, molecular simulation, or materials informatics

Nice To Haves

Experience building multi-agent or LLM-orchestrated systems for scientific applications
Familiarity with atomistic simulation tools (VASP, Gaussian, LAMMPS, ASE) or cheminformatics libraries
Publications at peer-reviewed ML or domain conferences
Experience with retrieval-augmented generation, knowledge graphs, or scientific literature mining
Understanding of crystal structure prediction, molecular dynamics, or quantum chemistry workflows
Experience with HPC job orchestration

Responsibilities

Design, implement, and optimize LLM-orchestrated multi-agent systems for autonomous materials discovery pipelines
Build specialized AI sub-agents that operate within a closed-loop discovery framework
Integrate agentic AI workflows with computational chemistry tools (DFT, MD, Monte Carlo) and HPC infrastructure
Develop and fine-tune retrieval-augmented generation (RAG) systems over scientific literature corpora for real-time knowledge synthesis
Collaborate with materials scientists, computational chemists, and ML researchers to translate domain workflows into autonomous agent architectures
Evaluate and benchmark agent performance on materials discovery tasks — measuring accuracy, throughput, and synthetic viability of generated candidates
Contribute to open-source tooling and publish research at top-tier venues (NeurIPS, ICML, ICLR, or domain journals)