Senior Data Center Architect for AI Deployments

AI Chopping Block; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

OpenAI is seeking a senior Data Center Infrastructure Architect to design and optimize the physical infrastructure for large-scale AI deployments. The role spans data center architecture, electrical and mechanical systems, high-density compute, and digital modeling to evaluate designs and improve efficiency, reliability, cost, and speed of deployment.

The ideal candidate will move between first-principles analysis, facility design, computational modeling, and real-world implementation,

Qualifications

• Significant experience designing or optimizing hyperscale data centers or large facilities.
• Broad knowledge of data center electrical and mechanical systems.
• Experience with simulation, digital-twin, or physics-based modeling.
• Strong understanding of data center efficiency metrics.

Responsibilities

• Define system-level architectures for high-density AI data centers.
• Develop digital twins and models of data center systems.
• Improve PUE, capacity, reliability, and deployment schedules.
• Evaluate tradeoffs in electrical topology, cooling, rack density.
• Translate AI hardware requirements into facility architectures.
• Lead technical reviews with cross-functional teams.
• Integrate telemetry into modeling and optimization workflows.
• Shape the technical roadmap for Industrial Compute products.

Skills

Hyperscale data centers

System-level design

Simulation modeling

Data center efficiency

Telemetry interpretation

Cross-disciplinary collaboration

Education

Bachelors in engineering

Tools

Modelica

MATLAB/Simulink

Python

EnergyPlus

CFD tools

Job description

OpenAI is seeking a senior Data Center Infrastructure Architect to design and optimize the physical infrastructure for large-scale AI deployments. The role spans data center architecture, electrical and mechanical systems, high-density compute, and digital modeling to evaluate designs and improve efficiency, reliability, cost, and speed of deployment.

The ideal candidate will move between first-principles analysis, facility design, computational modeling, and real-world implementation,