Full Stack Engineer, Fleet Scheduling

Position Overview

The Fleet Scheduling team at OpenAI builds intuitive and scalable interfaces that empower researchers to efficiently manage AI workloads across some of the world’s largest supercomputers. In this 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 researchers, product, and infrastructure teams to deliver scalable solutions for monitoring, job scheduling, and resource management. This position is based in San Francisco, CA, follows a hybrid work model (3 days in the office per week), and offers relocation assistance.

Key Responsibilities

• 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.

Required Qualifications

• Significant experience in full-stack development with modern frontend frameworks (React, Vue, or Angular) and backend technologies (Python, Go, or Node.js).
• Experienced in building scalable, high-performance web applications for complex distributed systems.
• Strong understanding of RESTful and GraphQL APIs, distributed databases, and cloud infrastructure (especially Azure).
• Execution-focused with a keen eye for usability, performance, and scalability in enterprise-scale systems.
• Comfortable working in fast-paced, highly collaborative environments with tight timelines and evolving priorities.

Preferred Qualifications

• Experience working with Kubernetes, Docker, and cloud-native application deployment.
• Understanding of AI/ML workload scheduling and orchestration challenges.
• Experience with real-time data processing, visualization libraries, and observability tooling.

Benefits & Perks

• Hybrid work model: 3 days in the office per week
• Relocation assistance
• Compensation: $325K – $490K + Offers Equity