OpenAI
Data Center Infrastructure Architect
- Jenis pekerjaan
- Penuh waktu
- Lokasi
- San Francisco
- Dipublikasikan
- 25 Okt 2026
- Terakhir diperiksa
- 26 Okt 2026
Tentang posisi ini
About the Team
OpenAI’s Industrial Compute team is building and productizing infrastructure capabilities that help organizations deploy and operate advanced AI systems at scale. The team works across AI hardware, systems engineering, physical infrastructure, and customer delivery to turn emerging technologies into reliable, repeatable infrastructure solutions.
Our work sits at the intersection of technical strategy, product development, engineering, and deployment. We partner closely with customers and internal engineering teams to solve complex infrastructure challenges spanning compute, power, cooling, controls, and facility efficiency.
About the Role
We are seeking a senior, hands-on Data Center Infrastructure Architect to develop and optimize the physical infrastructure required for large-scale AI deployments.
This is a broad technical role spanning data center architecture, electrical and mechanical systems, high-density compute, controls, telemetry, and digital modeling. You will use simulation, operational data, and digital-twin approaches to evaluate infrastructure designs, identify system-level constraints, and improve efficiency, reliability, cost, and speed of deployment.
The ideal candidate can move fluidly between first-principles analysis, facility and equipment design, computational modeling, engineering review, and real-world implementation. You should be comfortable working across disciplines rather than operating solely within electrical, mechanical, or software boundaries.
Key Responsibilities
- Define system-level architectures for high-density AI data centers across power, cooling, IT equipment, controls, and facility infrastructure.
- Develop digital twins and other computational models that represent the behavior of data center systems under changing workloads, environmental conditions, equipment configurations, and failure scenarios.
- Use design and operational data to identify constraints, improve PUE and related efficiency metrics, and optimize capacity, reliability, water consumption, cost, and deployment schedules.