Product Designer, Engineering Acceleration

Job description

About the Team OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. The Engineering Acceleration team builds products that multiply the effectiveness of OpenAI’s technical teams, helping engineers, researchers, and product teams understand complex systems, learn from what they ship, and operate reliably at scale. As AI changes how software is built, we have an opportunity to rethink engineering workflows from first principles. We’re creating tools and shared systems that turn complex data, experimentation, and technical workflows into clear decisions and useful action. About the Role In this role, you’ll lead design across two connected product areas: a real-time data exploration and observability experience for investigating large-scale system and product behavior, and an experimentation platform for safely launching changes, measuring their impact, and deciding whether to ramp, iterate, or roll back. This is more than a dashboard-design role. You’ll define the interaction models that take someone from a vague question or unexpected signal to a trustworthy answer and clear next step. You’ll work closely with engineers, researchers, data scientists, and product teams to understand the mechanics of their work and make dense technical systems coherent without flattening the details that matter. You’ll also help establish greater consistency across OpenAI’s enterprise and internal tools, developing durable patterns that support AI-native workflows and enable other designers to build more effectively. This role is based in our Seattle, WA or San Francisco, CA offices. We offer relocation assistance to new employees. In this role, you will: Lead end-to-end design for data-intensive products used by engineers, researchers, and product teams. Shape the complete learning loop: instrument, launch, observe, investigate, evaluate, decide, and iterate. Create clear, high-craft workflows for querying, filtering, comparison, drill-down, visualization, alerting, and sharing. Make complex concepts—including data freshness, coverage, reliability, uncertainty, experiment assignment, metric validity, rollout state, and regressions—understandable and actionable. Partner closely with engineering, data science, research, and product management to translate deeply technical infrastructure into elegant, coherent product experiences.…