Research Scientist, Advanced Control

Meta•Menlo Park, CA

1d•$184,000 - $257,000

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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