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.
Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.
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 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 of8 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 goodat 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.
