Engineering Manager, Library

About the team

The ChatGPT Library team is building the persistence layer that helps people and organizations collaborate with increasingly capable AI systems. ChatGPT Library provides users with a durable place for their files, context, and creations, forming a substrate between humans and agents—a foundation for AI that can remember, retrieve, and act on the right context over time.

The team sits within the Personalization organization and partners closely with product, design, research, model, and infrastructure teams. Its work helps models become a useful second brain for individuals—for example, answering questions such as “What did I work on last week?”—and supports enterprise use cases that turn company knowledge into accessible, actionable context.

About the role

We are looking for a hands-on engineering manager to lead a team of approximately 9–10 engineers and own execution across the product roadmap. This is a technical leadership role for someone who can set a high product bar, build a strong team, shape architecture, and contribute directly when needed. You will form and translate an ambitious product vision into a focused roadmap and help the team ship reliable, intuitive experiences at the intersection of AI, knowledge, and collaboration.

The ideal candidate leads by example. You are comfortable moving between people leadership, product decisions, technical design, and code. Founder or early-stage startup experience is especially valuable because the role requires urgency, sound judgment under ambiguity, and a willingness to operate across boundaries.

In this role you will

• Lead, coach, and grow a team of approximately 9–10 engineers while creating clarity, accountability, and a healthy execution rhythm.

• Own the engineering roadmap for Library, working with product and design partners to prioritize the highest-impact problems.

• Set a high bar for product quality, technical excellence, reliability, privacy, and user trust.

• Provide hands-on technical leadership through architecture reviews, prototyping, debugging, and coding when it will accelerate the team or unblock a critical path.

• Build systems that allow AI products to persist, retrieve, and use personal and organizational context effectively.

• Partner across Personalization, model, research, infrastructure, enterprise, and third-party integration teams to deliver cohesive end-to-end experiences.

• Navigate ambiguity, make pragmatic tradeoffs, and turn emerging AI capabilities into a clear product and engineering plan.

• Hire exceptional engineers and develop technical leaders who can scale the team’s impact.

You might thrive in this role if you

• Have experience managing and developing high-performing engineering teams while remaining technically hands-on.

• Have a strong software engineering foundation and are comfortable contributing production code in a modern codebase.

• Have built and shipped user-facing products with a high quality bar, ideally in fast-moving or zero-to-one environments.

• Bring founder, startup, or early-stage product experience and know how to create momentum with limited structure.

• Can reason about distributed systems, data persistence, retrieval, permissions, collaboration, and the product implications of AI-native architectures.

• Communicate clearly across engineering, product, design, research, and executive stakeholders.

• Balance long-term technical direction with near-term delivery and know when to simplify, prototype, or invest for scale.

• Care deeply about building AI products that are useful, trustworthy, and intuitive for both individuals and organizations.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created wit

Responsibilities

• Lead, coach, and grow a team of approximately 9–10 engineers while creating clarity, accountability, and a healthy execution rhythm.
• Own the engineering roadmap for Library, working with product and design partners to prioritize the highest-impact problems.
• Set a high bar for product quality, technical excellence, reliability, privacy, and user trust.
• Provide hands-on technical leadership through architecture reviews, prototyping, debugging, and coding when it will accelerate the team or unblock a critical path.
• Build systems that allow AI products to persist, retrieve, and use personal and organizational context effectively.
• Partner across Personalization, model, research, infrastructure, enterprise, and third-party integration teams to deliver cohesive end-to-end experiences.
• Navigate ambiguity, make pragmatic tradeoffs, and turn emerging AI capabilities into a clear product and engineering plan.
• Hire exceptional engineers and develop technical leaders who can scale the team’s impact.

Requirements

• Have experience managing and developing high-performing engineering teams while remaining technically hands-on.
• Have a strong software engineering foundation and are comfortable contributing production code in a modern codebase.
• Have built and shipped user-facing products with a high quality bar, ideally in fast-moving or zero-to-one environments.
• Bring founder, startup, or early-stage product experience and know how to create momentum with limited structure.
• Can reason about distributed systems, data persistence, retrieval, permissions, collaboration, and the product implications of AI-native architectures.
• Communicate clearly across engineering, product, design, research, and executive stakeholders.
• Balance long-term technical direction with near-term delivery and know when to simplify, prototype, or invest for scale.
• Care deeply about building AI products that are useful, trustworthy, and intuitive for both individuals and organizations.