Senior Engineering Manager, Core AI Platform

Senior Engineering Manager, Core AI Platform November 10, 2025 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 12-2 0 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 2 0 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 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 aboveand 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. #J-18808-Ljbffr