IC Agentic Engineering Manager - Stargate

About the Team OpenAI’s Stargate Infrastructure team is building and operating the systems that power next-generation AI workloads at massive scale. This includes deploying and managing clusters, networks, and data center infrastructure across first-party and partner environments. As the scale and complexity of these systems grow, we are investing in agentic systems and intelligent automation to improve how infrastructure is deployed, operated, and debugged. This team focuses on applying AI-driven approaches to real-world infrastructure workflows—enabling faster execution, higher reliability, and scalable operations. About the Role We are seeking an IC Agentic Engineering Manager to lead the development and application of agent-based systems for infrastructure delivery and operations within Stargate. This is a player-coach role: you will contribute directly to system design and implementation while leading a small team. You will focus on applying agentic systems to infrastructure workflows such as deployment orchestration, system bring-up, issue triage, debugging, and capacity management. This role is not focused on building general-purpose agent platforms. Instead, it is centered on applying agentic systems to solve concrete infrastructure problems, working closely with hardware, networking, and cluster teams. Key Responsibilities - Design and build agent-based systems to support infrastructure deployment and operations - Identify high-impact opportunities to apply agents across workflows such as: - cluster bring-up and deployment readiness - incident triage and root cause analysis - system validation and health monitoring - capacity management and operational decision-making - Lead a small team while contributing directly as an IC across system design, development, and integration - Partner with infrastructure, hardware, and networking teams to integrate agentic systems into production workflows - Develop systems that leverage telemetry, logs, and system signals to enable closed-loop automation - Define evaluation frameworks to measure system effectiveness, reliability, and operational impact - Drive iteration from prototype to production, ensuring robustness and scalability Qualifications - Strong software engineering background in distributed systems, infrastructure, or platform engineering - Experience building production automation systems or data-driven operational tooling - Experience applying AI, ML, or agent-based approaches to real-world systems or workflows - Ability to operate as a hands-on IC while leading a small team - Experience working cross-functionally with infrastructure, hardware, or systems teams - Strong problem-solving skills in complex, ambiguous environments Preferred Skills - Experience with LLM-based systems, agents, or autonomous workflows - Background in infrastructure operations, SRE, or large-scale system deployment - Experience working on cluster bring-up, debugging, or data center infrastructure systems - Familiarity with telemetry, monitoring systems, and observability pipelines - Experience building internal tools or platforms for engineering productivity and operations About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and no

Strong software engineering background in distributed systems, infrastructure, or platform engineering. Experience building production automation systems or data-driven operational tooling. Experience applying AI, ML, or agent-based approaches to real-world systems or workflows. Ability to operate as a hands-on IC while leading a small team. Experience working cross-functionally with infrastructure, hardware, or systems teams. Strong problem-solving skills in complex, ambiguous environments.

Design and build agent-based systems to support infrastructure deployment and operations. Identify high-impact opportunities to apply agents across workflows such as cluster bring-up and deployment readiness, incident triage and root cause analysis, system validation and health monitoring, capacity management and operational decision-making. Lead a small team while contributing directly as an IC across system design, development, and integration. Partner with infrastructure, hardware, and networking teams to integrate agentic systems into production workflows. Develop systems that leverage telemetry, logs, and system signals to enable closed-loop automation. Define evaluation frameworks to measure system effectiveness, reliability, and operational impact. Drive iteration from prototype to production, ensuring robustness and scalability.