Tech Lead, Deployment & Operations — Custom Infrastructure
About the role
About
the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads.
The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure,
the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.
About
the Role We are seeking a Technical Lead to lead deployment and operations for OpenAI’s Silicon & Systems team. This person will become the Directly-Responsible Individual responsible for bringing OpenAI’s custom silicon and associated systems into data center environments, ensuring successful deployment, bring-up, validation, operational readiness, and ongoing reliability at scale. This role sits at the intersection of silicon, systems, infrastructure, data center operations, and software. You will lead a team focused on taking new hardware platforms from lab validation into production data center deployment. You will be responsible for building the operational processes, technical workflows, tooling, and cross-functional alignment required to deploy and operate custom AI hardware reliably in OpenAI’s supercomputing infrastructure. The ideal candidate is both a strong leader and a deeply technical operator. You should be comfortable staying close to the technical details of hardware bring-up, fleet deployment, debugging, system validation, data center integration, and production operations. This role requires strong execution, excellent cross-functional judgment, and the ability to drive clarity in ambiguous, fast-moving environments. In this role, you will: Lead a team responsible for deployment and operations of OpenAI’s custom silicon and systems in data center environments Own the path from hardware bring-up and validation through production deployment, operational readiness, and sustained fleet support Partner closely with silicon, systems, software, infrastructure, networking, data center, supply chain, and external partner teams to ensure successful deployment at scale Define deployment processes, operational playbooks, technical readiness criteria, escalation paths, and reliability practices for new hardware platforms Drive cross-functional execution across lab bring-up, rack/system integration, data center deployment, fleet monitoring, debugging, and issue resolution Stay hands-on technically through architecture reviews, deployment planning, failure analysis, operational debugging, and critical system-level decision-making Identify gaps in tooling, observability, automation, validation coverage, and operational processes, and build plans to close them Establish clear metrics for deployment readiness, reliability, performance, maintainability, and operational health Build a strong engineering culture grounded in ownership, technical rigor, operational excellence, and high-velocity execution Ensure OpenAI’s custom hardware platforms can be deployed and operated reliably, repeatably, and safely at scale Be a contributor and technical driver for the architecture and design of future ML systems You might thrive in this role if you: Enjoy mentoring and developing engineers while staying deeply engaged in technical execution Are excited by the challenge of bringing new custom hardware platforms into real-world production data center environments Can operate across silicon, systems, software, infrastructure, and data center operations Are comfortable leading through ambiguity, especially when the hardware, tooling, and operational model are still being built Have strong judgment around deployment sequencing, technical risk, operational readiness, and when to escalate Communicate clearly across technical and operational teams, and can align stakeholders through complex deployment and production issues Care deeply about building practical systems, tools, and processes that work reliably at scale Have a bias toward ownership and are comfortable jumping into urgent technical issues when needed
