Engineering Manager, Artifacts

OpenAI; San Francisco (CA); On-site; USD 250,000 - 350,000; Full time

Job summary

Jobtailor seeks an experienced engineering leader to build and grow AI-native artifact creation experiences across documents, spreadsheets, slide decks, and emerging formats.

You will set the technical direction for full-stack systems, model integration, and reliability, while partnering with research, design, and safety teams. The role requires hands-on architecture and strategic planning for the team’s next phase.

Qualifications

• Experience leading engineering teams while remaining technically close to the work.
• Strong full-stack product engineering fundamentals across frontend, backend, infrastructure, and model-facing systems.
• Interest in AI-native creation tools and useful documents, slides, spreadsheets, dashboards, and interactive artifacts.
• Ability to operate in ambiguous, fast-moving environments with evolving products, model capabilities, and technical architecture.
• Experience partnering with research, ML, product, design, infrastructure, or data teams.
• Strong focus on craft, quality, latency, reliability, and user experience.

Responsibilities

• Lead, manage, and grow the engineering team building AI-native artifact creation experiences across documents, spreadsheets, slide decks, and emerging formats.
• Set technical direction across full-stack product systems, generation orchestration, editing and rendering surfaces, storage, reliability, and model integration.
• Own hands-on architecture, code review, debugging, system design, and critical product decisions.
• Partner with research teams to translate model capabilities, training needs, evaluations, and behavioral insights into shipped product improvements.
• Work with product, design, infrastructure, and safety partners to define the artifact creation user experience.
• Create the engineering plan for the team's next phase, 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.
• Expand the team's scope from familiar artifact types into new forms of AI-native work

Skills

Team Leadership

System Design

Model Integration

Full-Stack Engineering

Latency Optimization

Reliability Engineering

Technical Direction

Artifact Creation

Clear Communication

Job description

• Lead, manage, and grow the engineering team building AI-native artifact creation experiences across documents, spreadsheets, slide decks, and emerging formats
• Set technical direction across full-stack product systems, generation orchestration, editing and rendering surfaces, storage, reliability, and model integration
• Own hands-on architecture, code review, debugging, system design, and critical product decisions
• Partner with research teams to translate model capabilities, training needs, evaluations, and behavioral insights into shipped product improvements
• Work with product, design, infrastructure, and safety partners to define the artifact creation user experience
• Create the engineering plan for the team's next phase, 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
• 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 across frontend, backend, infrastructure, and model-facing systems
• Interest in AI-native creation tools and useful documents, slides, spreadsheets, dashboards, and interactive artifacts
• Ability to operate in ambiguous, fast-moving environments with evolving products, model capabilities, and technical architecture
• Experience partnering with research, ML, product, design, infrastructure, or data teams
• Strong focus on craft, quality, latency, reliability, and user experience
• Strong judgment about product-specific systems versus reusable platform foundations
• Ability to learn quickly and communicate clearly
• Technical depth sufficient to raise the team's engineering bar
• Ability and desire to define and own a new category of AI-native work
• Ability to work from the US office three days per week

Core Competencies

Demonstrates expertise in leading engineering teams while maintaining technical involvement, with a strong foundation in full-stack product engineering and a focus on AI-native creation tools. Capable of balancing product velocity with quality and reliability, while effectively collaborating with cross-functional teams.

Highest-signal resume keywords

• Full-Stack Product Engineering
• AI-Native Creation Tools
• Team Leadership
• System Design
• User Experience

ATS Optimization Keywords

Hard Skills

• Architecture
• Code Review
• Debugging
• System Design
• Model Integration
• Product Engineering Fundamentals
• Latency Optimization
• Reliability Engineering
• Technical Direction
• Artifact Creation

Soft Skills

• Clear Communication
• Judgment
• Adaptability
• Collaboration
• Learning Agility

Industry Keywords

• AI-Native Work Products
• Engineering Management
• Product Development
• Research Collaboration
• Interactive Artifacts