Full Stack Engineer, Fleet Scheduling

About the Team Full Stack engineers within the Fleet Scheduling team are dedicated to building intuitive and scalable interfaces that empower researchers to efficiently manage AI workloads across some of the largest supercomputers in the world. Our focus is on developing robust, high-performance systems that provide real-time insights, resource tracking, and seamless interaction with complex infrastructure. We aim to optimize resource allocation, minimize operational overhead, and create user-friendly tools that enhance researcher productivity and system transparency. About the Role You will design, develop, and operate web-based systems that provide a powerful and intuitive interface to OpenAI’s supercomputing clusters. You will collaborate closely with researcher, product and infrastructure teams to deliver scalable solutions that enable seamless monitoring, job scheduling, and resource management. This is an opportunity to work at the cutting edge of AI infrastructure, designing tools that scale to exascale workloads while maintaining usability and performance. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design and develop full-stack web applications to track, monitor, and manage large-scale AI workloads in real time. - Collaborate with researchers and infrastructure teams to translate complex operational needs into intuitive UIs and scalable backends. - Build data visualization tools (e.g., Gantt charts, dashboards) to provide insights into job scheduling and resource allocation. - Optimize backend services to handle massive data throughput while ensuring low-latency performance and high availability. - Implement frontend components that provide seamless interactions with scheduling, storage, and compute systems. - Ensure system security, reliability, and scalability across globally distributed supercomputing infrastructure. You mi

Department: Scaling; Team: Fleet Clusters