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.
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
Minimum Qualifications
- 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