Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $347K - $405K, with generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
Benefits include:
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends
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. This team works across product, infrastructure, and research, partnering 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.
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.
Responsibilities
- 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.
Requirements
- Experience leading engineering teams while remaining technically close to the work.
- Strong full-stack product engineering fundamentals and can reason across frontend, backend, infrastructure, and model-facing systems.
- 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.
- 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.
- 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.