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OpenAI's Artifacts team is building the AI-native creation layer for documents, spreadsheets, slide decks, dashboards, reports, and interactive work products. The team rethinks creation when AI models can move from ambiguous user goals to polished, editable artifacts with strong structure and correctness.
As Engineering Manager, Artifacts, you will lead and grow the engineering team responsible for building this product and technical foundation. You'll manage a team of full-stack and infrastructure-oriented engineers, set technical direction, and remain hands-on enough to shape architecture and debug hard problems.
This role sits at the intersection of product engineering, research, and infrastructure. You will partner with researchers on model training and evaluation for artifact creation, with product and design on user experience, and with infrastructure teams on platform reliability.
Key responsibilities include:
- Leading, managing, and growing a team building AI-native artifact creation experiences across multiple formats
- Setting technical direction across full-stack product systems, generation orchestration, editing and rendering surfaces, storage, reliability, and model integration
- Hands-on architecture, code review, debugging, system design, and critical product decisions
- Partnering with research teams to translate model capabilities and training needs into shipped product improvements
- Working with product, design, infrastructure, and safety partners to define excellent artifact creation experiences
- Creating engineering plans for the next phase including hiring, execution milestones, and technical investments
- Balancing near-term product velocity with long-term platform quality, reliability, and extensibility
- Debugging complex failures across model behavior, product surfaces, infrastructure, and user-facing quality
- Expanding the team's scope from familiar artifact types into new forms of AI-native work products
Ideal candidates have experience leading engineering teams while remaining technically close to the work, strong full-stack product engineering fundamentals, and excitement about AI-native creation tools. You should operate well in ambiguous, fast-moving environments where product, model capability, and technical architecture evolve simultaneously. Experience partnering with research, ML, product, design, or infrastructure teams is valuable. You care about craft, quality, latency, reliability, and user experience, with strong judgment about building product-specific systems versus reusable platform foundations.
