Product Designer, Engineering Acceleration
OpenAI
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.
- Use prototyping, research, and rigorous design exploration to establish interaction patterns for emerging technical and AI-native workflows.
- Define shared systems and patterns that create consistency across OpenAI’s enterprise and internal tools.
- Establish a high bar for information architecture, visual hierarchy, interaction quality, and craft across complex, multi-surface products.
- Have substantial experience owning data products, observability platforms, experimentation systems, analytics products, developer tools, AI/ML tooling, or similarly technical workflows.
- Are a strong product and systems thinker who can set direction in an ambiguous space and make durable interaction and platform decisions.
- Can make dense information feel calm, legible, and useful without oversimplifying important complexity.
- Are technically fluent, enjoy learning how underlying systems work, and can build credibility with engineers and other highly technical users.
- Have strong instincts for trust and understand how provenance, reliability, uncertainty, permissions, and edge cases affect whether people act on a system’s output.
- Have redesigned workflows spanning multiple tools, products, or surfaces and can create coherence across a broader ecosystem.
- Have strong UX/UI and interaction design skills, with a high bar for contemporary visual craft and information design.
- Can demonstrate how your work changed product decisions, accelerated learning, or improved the velocity of the teams you supported.
- Enjoy staying close to detailed workflows and hands-on design while helping shape a broader product vision.
- Are excited to define how engineers and AI agents will build, evaluate, and improve software together.
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