Senior Engineering Manager, Core AI Platform

Role Description

At Dropbox, we believe in simplifying the way people work together. We provide a range of innovative cloud-based solutions to empower individuals and businesses to share, access, and collaborate on their files seamlessly. Engineering Managers are pivotal in shaping our mission of building a more enlightened way of working where everyone can unleash their creative potential without constraints.

As a Senior Engineering Manager, you’ll thrive in our team if you love chasing impact, working through ambiguity, and developing a culture of innovation. In this role, you’ll lead a team of 12-20 engineers, leveraging your robust managerial toolbox to drive direct business and customer impact independently. You’ll work closely with your direct reports, cross-functional partners, and other teams to build the future of Dropbox. Our team culture rewards a bias for action, engineering partnership in defining our strategy, and efficient operational excellence. The ideal candidate will possess strong leadership skills, technical expertise, and a passion for driving results in a collaborative, virtual-first environment.

On our Core AI Platform team, you will be responsible for leading AI integrations into Dropbox’s flagship File Sync & Share product. This team is responsible for integrating the Dash product into our existing file-focused workflows and for our existing Dropbox customers, as well as evolving Dash to become a platform product. This team will create a new platform/API abstraction to enable all Core teams easily integrate with AI-powered capabilities and build agents leveraging Dash and other technologies. This is a high visibility role leading a high priority for Dropbox and has the opportunity to shape our future company direction, as we evolve the Dropbox product from sync and storage to an AI-powered workspace.

Responsibilities

• Directly manage up to 20 direct reports, as a single execution unit
• Partner with multiple Product Managers to plan roadmaps and sprints
• Be accountable for high quality execution on critical and high visibility business objectives
• Be responsible for making tradeoffs between feature work and addressing long term technical debt
• Be responsible for excellence in software quality, engineering practices and operations
• Lead the team through standard Agile processes, such as backlog management, stand ups, retros, etc.
• Review and approve engineering designs and be able to set technical direction (in collaboration with a Technical Lead)
• Drive cross-org initiatives alongside managing execution responsibilities
• Drive career conversations and career plans for your direct reports 

Requirements

• BS, MS, or PhD in Computer Science or a related technical field involving coding (e.g., physics or mathematics) or equivalent technical experience
• Minimum of 8 years of people management experience with an engineering team
• Minimum of 10 years as a software engineer or equivalent technical experience
• Must have experience managing engineering organizations of 20 or above and experience managing managers
• Must have experience building or integrating with AI products
• Must have worked in a platform team or a developer-facing API product with both internal and external consumers
• Must have experience running a standard Agile process such as backlog management, stand ups, retros, etc
• Must be results-driven, especially good at balancing execution predictability with the agility needed in bringing a new product to market

Preferred Qualifications

• Experience shipping AI-powered features in production, collaborating closely with ML engineers and product teams
• Strong backend engineering skills with systems that support model serving, inference, and data pipelines at scale
• Strong background in AI agent infrastructure and building AI agents
• Ability to evaluate AI models for a specific use case and advise on build vs buy decisions
• Expertise in distributed systems architecture — designing for scalability, fault tolerance, observability, and graceful degradation in multi-service environments.