Engineering Manager, Artifacts

ABOUT THE TEAM

The Artifacts team is building the AI-native creation layer for documents, spreadsheets, slide decks, dashboards, reports, analyses, and new forms of interactive work products. We are rethinking what creation looks like when models can move from an ambiguous user goal to a polished, editable artifact with strong structure, taste, correctness, and speed.

This is a high-agency team working across product, infrastructure, and research. We partner closely with model training teams to shape how frontier models create artifacts, and with ChatGPT product teams to turn those capabilities into experiences that millions of people can use. The work spans full-stack product engineering, model integration, rendering and editing systems, collaboration, storage, evaluation loops, and production reliability.

Our ambition is to build the premier product experience for AI-generated artifacts: starting with familiar work products like slides, sheets, and docs, then expanding into new artifact types that are only possible in an AI-native world.

ABOUT THE ROLE

As Engineering Manager, Artifacts, you will lead and grow the engineering team responsible for building this product and technical foundation. You will manage a team of full-stack and infrastructure-oriented engineers, set technical direction, and stay 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 how models are trained and evaluated for artifact creation, with product and design on the user experience.

This is a strong fit for a technical manager who wants to build and ship, not only coordinate. The team has a fast trajectory, so you will help define both the product surface and the team that builds it.

IN THIS ROLE, YOU WILL:

- Lead, manage, and grow a team building AI-native artifact creation experiences across documents, spreadsheets, slide decks, and emerging artifact formats.

- Set technical direction across full-stack product systems, generation orchestration, editing and rendering surfaces, storage, reliability, and model integration.

- Responsible for hands-on architecture, code review, debugging, system design, and critical product decisions.

- Partner closely with research teams to translate model capabilities, training needs, evals, and behavioral insights into shipped product improvements.

- Work with product, design, infrastructure, and safety partners to define what excellent artifact creation should feel like for users.

- Create the engineering plan for the next phase of the team, including hiring, execution milestones, technical investments, and operating cadence.

- Balance near-term product velocity with long-term platform quality, reliability, extensibility, and developer productivity.

- Debug complex failures across model behavior, product surfaces, infrastructure, latency, and user-facing quality.

- Help expand the team’s scope from familiar artifact types into new forms of AI-native work products.

YOU MIGHT THRIVE IN THIS ROLE IF YOU:

- Have experience leading engineering teams while remaining technically close to the work.

- Have strong full-stack product engineering fundamentals and can reason across frontend, backend, infrastructure, and model-facing systems.

- Are excited by AI-native creation tools and have opinions about what makes documents, slides, spreadsheets, dashboards, and interactive artifacts genuinely useful.

- Can operate in ambiguous, fast-moving environments where the product, model capability, and technical architecture are all evolving at once.

- Have experience partnering with research, ML, product, design, infrastructure, or data teams.

- Care about craft, quality, latency, reliability, and user experience, not just whether a system technically works.

- Have strong judgment about when to build product-specific systems versus reusable platform foundations.

- Learn quickly, communicate clearly, and bring enough technical depth to raise the bar for the team.

- Desire to help define and own a new category of AI-native work.