VP, AI Marketing Strategy

MongoDB•Palo Alto, CA

2d•$145,000 - $285,000•Hybrid

About The Position

MongoDB serves as the data layer powering the most important AI applications in the world today. This role exists to ensure the market knows what MongoDB does and to secure the next generation of builders before they default to another platform. The VP of AI Marketing Strategy & Ecosystem will shape the training data, documentation, integrations, and ecosystems that determine what gets built and how, thereby positioning MongoDB as the generational data platform for agentic applications. Builders and agents alike need to reach for MongoDB by default. That requires presence in the systems and tools that influence how applications get built, not just the channels that reach the humans building them. This position will report to the CMO and be part of the Marketing Leadership Team. The VP of AI Marketing Strategy & Ecosystem will work closely with the Chief Product Officers, Chief Technology Officer, Chief Customer Officer, and regional marketing leaders. This role elevates AI visibility and representation, ecosystem partnerships, and developer content into a single VP seat, working closely with product and partner teams to align efforts that currently span the company. This role can be based out of our San Francisco or Palo Alto offices, or remotely in the region.

Requirements

You have operated at the intersection of AI and marketing at a level most marketing leaders haven't reached; You may have been a head of marketing or CMO at an AI company, a developer tools company, or a foundation model provider; You understand the ecosystem from the inside, not from a distance
You build with the tools you're asking your team to use; You are fluent in agentic workflows, e.g., Claude Code, Cursor, or equivalent; You can direct agents to produce content, run programs, and ship at a pace traditional marketing orgs can't match; You don't delegate this fluency; You model it
You think about content as infrastructure; You have made deliberate decisions about what gets indexed, surfaced, cited, and trained on; You understand that documentation quality, code example density, and dataset presence are distribution levers, not support functions
You are obsessed with developer discovery; You wake up thinking about how a developer or an agent encounters MongoDB for the first time, through a search result, an LLM citation, a framework recommendation, or an agent tool call; You know the difference; You have a strategy for each
Technical enough to be credible with engineers, product leaders, and technical partners; You don't need to write code; you just need to understand how LLMs are built, how retrieval works, and how agents select tools; You can sit with an engineering team and know what questions to ask
You have a partnership instinct; You identify emerging frameworks and companies before they're obvious and move fast to establish presence before defaults are set; When a new agent framework launches, you're already in the room
Experience influencing how a product is represented in systems you don't control, through documentation strategy, training data presence, API/integration design, or developer ecosystem positioning; You have done this deliberately, not accidentally
You've shipped something (i.e., a system, a channel, a program) that changed how a technical audience found or chose a product; You can point to it and explain exactly what you did
You have owned a content, documentation, or developer marketing function and have a measurable outcome to show for it; Not a strategy deck. An outcome
Track record of holding cross-functional partners accountable without a formal reporting line; You'll bring product-owned content under the same accuracy standard as marketing's, without owning the reporting line
A builder and an operator; Small team, high visibility, hands-on role

Responsibilities

AI market narrative: own the strategic case for MongoDB as the default data platform for agentic applications; ensure the story holds up under scrutiny from analysts and competitors; and ensure it resonates with builders. Partner with Product Management and Product Marketing to keep the AI story integrated into MongoDB's core narrative, not a separate one
AI visibility and representation: own how MongoDB appears in AI-generated responses, agent framework recommendations, and developer tool suggestions; This goes beyond content; It includes documentation quality, technical accuracy, and the underlying infrastructure that determines how MongoDB is represented across AI systems and training data
Ecosystem integration presence: own MongoDB's presence and prominence inside the tools and environments where builders work; This includes the marketing side of integrations and the ecosystem relationships that make them land; You'll define final scope and working ownership with the existing partner and revenue marketing leaders in your first 90 days
AI ecosystem partnerships: own co-marketing with MongoDB's key AI ecosystem partners across agent frameworks, deployment platforms, and frontier model providers; When a builder reaches for a framework, MongoDB is already there; Execution ownership for existing partner motions is sorted with the relevant marketing leads once you're in the seat
AI accuracy standard: set the bar for how MongoDB's product and technical content should be interpreted by AI systems, including training data, documentation, and code examples; You don't own the labor of rewriting content; You own whether it's right, and you hold marketing’s content teams across the company accountable to the standard; Content owned by other teams (e.g., Product) plays by the same standard, and you bring those teams into the effort rather than run a parallel track
Agentic builder acquisition: own new paths to agentic builders and the systems they deploy; Set the AI-specific plays and priorities for regional marketing teams; You define the brief; They execute