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.
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)
Minimum Qualifications
- 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 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