Research Scientist, Advanced Control

At Meta IDC (Infrastructure Data Center), our goal is to deliver the trusted capacity that powers Meta's AI and products worldwide. The Physical Modeling team inside IDC develops physics-based and ML models to inform, de-risk, accelerate, and future-proof decisions across the IDC lifecycle. As Meta's data center fleet grows rapidly in scale and complexity, traditional control strategies are reaching their limits — they cannot effectively adapt to transient conditions, multi-system interactions, or next-gen configurations at the pace our fleet scales. We are looking for a technical leader with deep expertise in advanced control and AI to build on our existing modeling foundation — defining and driving the roadmap for intelligent control that improves efficiency, reliability, and sustainability at fleet scale. The chosen candidate will lead cross-functional initiatives spanning internal engineering teams and external industrial control system vendors to develop and deliver deployable, robust control strategies across Meta's data center fleet.

Define and own the advanced control roadmap in IDC, building on the team's existing physical modeling capabilities Shape the vision for intelligent, autonomous data center operations from advisory recommendations to governed autonomy at fleet scale Lead projects from problem framing through validated, deployment-ready solutions, translating ambiguous operational challenges into well-scoped research with clear success criteria Develop RL-based control strategies that enable self-optimizing data center systems — improving thermal stability, energy efficiency, and operational reliability in transient conditions Shape advanced control strategies into deployable solutions that align with Meta's system architecture, operational constraints, and deployment requirements Establish validation frameworks and safety guardrails that build operational trust Partner with internal engineering teams and external industrial control vendors to co-develop deployable advanced control solutions Drive cross-functional alignment on methodology, adoption, and integration with Meta's system architecture, operational constraints, and fleet-scale deployment challenges Represent advanced control capabilities to senior stakeholders, influencing investment and prioritization decisions

PhD in a science or engineering discipline 8+ years of experience spanning advanced control (e.g., MPC, optimal control, adaptive control, etc.), applied reinforcement learning or AI-driven control, and critical infrastructure control systems Technical leadership experience architecting and delivering research-to-production projects Working knowledge of mechanical, electrical, and thermal systems in industrial or critical infrastructure environments Demonstrated track record of leading interdisciplinary research and engineering initiatives across teams or organizations Experience communicating technical strategy to both technical and non-technical audiences Experience driving alignment in cross-functional, matrixed organizations Experience in data centers or critical MEP (Mechanical, Electrical, Power) infrastructure Experience with HVAC controls, Building Management Systems (BMS), or hardware-in-the-loop / software-in-the-loop validation Familiarity with digital twins or physics-based simulation as training environments for control Experience designing safety validation frameworks or advisory-to-autonomous control pipelines Experience applying reinforcement learning to physical systems or industrial control problems Familiarity with industrial control system architectures and the constraints they impose on control strategy design