# Tech Lead, Deployment & Operations — Custom Infrastructure

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Salary:** $342k – $445k/yr
**Experience:** 8+ years
**Skills:** Hardware Deployment, Data Center Operations, Silicon Bring-Up, Systems Validation, Fleet Operations, Reliability Engineering, Infrastructure Automation, Hardware/Software Integration, Deployment Planning, Incident Response, Root-Cause Analysis, Observability, Tooling, Automation, Rack-Scale Systems
**Posted:** 2026-05-16

> Lead deployment and operations for OpenAI’s custom silicon and systems into data center environments. Drive hardware bring-up, validation, production deployment, and fleet reliability at scale while leading a technical team.

## Job Description

## Responsibilities
- 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
- Be a contributor and technical driver for the architecture and design of future ML systems

## Requirements
- 8+ years of engineering experience in hardware systems, infrastructure, data center deployment, production operations, systems engineering, silicon bring-up, or related technical domains
- Strong technical depth in one or more of: hardware deployment, data center operations, rack-scale systems, silicon bring-up, systems validation, fleet operations, reliability engineering, infrastructure automation, or hardware/software integration
- Experience bringing complex hardware systems from development or validation into production environments
- Experience working closely with silicon, systems, software, infrastructure, networking, or data center teams
- Experience with deployment planning, operational readiness, incident response, debugging, and root-cause analysis for production systems
- Experience building tooling, automation, observability, or operational processes that improve deployment quality and fleet reliability
- Demonstrated ability to hire, develop, and lead senior technical talent
- Ability to move fluidly between people leadership, technical strategy, and hands-on operational problem solving
- Strong written and verbal communication skills, especially in high-urgency, cross-functional technical environments
- Experience working in fast-moving environments

## Nice-to-Haves
- Enjoy mentoring and developing engineers while staying deeply engaged in technical execution
- Excited by the challenge of bringing new custom hardware platforms into real-world production data center environments
- Comfortable operating across silicon, systems, software, infrastructure, and data center operations
- Comfortable leading through ambiguity, especially when the hardware, tooling, and operational model are still being built
- Strong judgment around deployment sequencing, technical risk, operational readiness, and when to escalate
- Care deeply about building practical systems, tools, and processes that work reliably at scale
- Bias toward ownership and comfortable jumping into urgent technical issues when needed

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