Role Description
Dropbox is building the knowledge layer that connects content, context, and action. As AI moves from assistants to systems that act, the structure and stewardship of enterprise knowledge becomes the difference between AI that helps and AI that fails.
This role owns the context layer our AI depends on: what it can know, what it can trust, and what it is allowed to act on. You will lead the central function, setting the strategy, architecture, standards, and operating model that make enterprise knowledge reliable, current, and permissions-aware for both human and AI use, while domain experts stay accountable for the accuracy of their content.
AI capability changes quickly, and this role changes with it. What a model can interpret, how content needs to be structured for retrieval, and what a system can safely act on all shift as the technology moves. You will track those shifts, translate them into practical standards, and revise your own past decisions when the ground moves under them.
This is the first role of its kind at Dropbox. You will partner with IT, Engineering, Legal, Privacy, and Security, and your decisions will show up directly in how AI performs across the company.
Responsibilities
- Define the source-of-truth strategy for enterprise knowledge: which systems are authoritative, what is indexed centrally versus fetched live, what is eligible for AI use, and what is archived or excluded, informed by an assessment of the authoritative sources behind our highest-value workflows.
- Define the enterprise standards that make content AI-ready across structure, metadata, provenance, and access, including where semantic models or knowledge graphs are warranted and where they are not, and translate them into authoring patterns adopted across domains.
- Design the control model for AI actions, including eligibility rules, preconditions, approval boundaries, escalation paths, and rollback requirements, so systems that act on enterprise knowledge stay traceable and safe as AI capabilities evolve.
- Lead platform and connector strategy across the content stack. Drive decisions on what is refactored, migrated, indexed in place, or consolidated, and partner with IT and Engineering on connector architecture and how AI systems are granted access to tools and sources.
- Build the federated operating model for enterprise content: stewardship across functions, domains accountable for their own accuracy within shared standards, and lifecycle policies covering review cadence, expiration, material-change triggers, and retirement, tied to business criticality.
- Define content quality in an AI context. Stand up retrieval and grounding evaluations for priority use cases, extend measurement to workflow traces and policy conformance as systems begin to act, and route findings back into the content lifecycle.
- Co-own the criteria for AI content eligibility, sensitivity classification, and permissions modeling with Legal, Privacy, and Security, including access boundaries for the tools AI systems can reach.
Requirements
- 7+ years designing how information is structured, owned, and maintained at enterprise scale, including at least 2 years applying that work to AI retrieval and grounding.
- Direct experience preparing content for AI consumption, with working fluency in retrieval-augmented generation, grounding, semantic chunking, embeddings, vector search, and citations.
- Hands-on experience with knowledge graphs, ontologies, or semantic models that structure content for machine consumption.
- Track record building federated operating models across functions outside direct reporting lines, with evidence of metadata standards or authoring frameworks adopted at scale.
- Demonstrated ability to influence senior stakeholders across Engineering, IT, Legal, Security, and business functions.
- Sound judgment on balancing central standards with domain expertise.
Preferred Qualifications
- Hands-on experience with enterprise platforms such as ServiceNow, Atlassian, Microsoft 365 or Copilot Search, Slack, or Notion.
- Familiarity with structured authoring (such as DITA), controlled vocabularies, or knowledge operations methodologies such as KCS.
- Experience with AI evaluation tooling and frameworks for measuring retrieval quality, groundedness, and answer relevance.
- Background working in regulated, policy-heavy, or high-risk content domains.
- Working knowledge of NIST AI RMF, OWASP GenAI guidance, or comparable risk frameworks.
Durable Skills
AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve:
- Awareness: Understand yourself and others.
- Judgment: Evaluate information and make decisions in complex situations.
- Adaptability: Learn, adjust, and stay effective through change.
- Connection: Communicate, collaborate, and build trust.
To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser.