Vice President of AI Marketing Strategy, MongoDB
$145-285k
Modern data platform
Be an early applicant
Modern data platform
1001+ employees
Be an early applicant
$145-285k
1001+ employees
Company mission
To empower innovators to create, transform, and disrupt industries by unleashing the power of software and data.
Role
Who you are
- 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
What the job involves
- 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
- 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
- Citation Count across AI-generated developer responses (the leading indicator for visibility, accuracy, and selection)
- Accuracy Rate: how correctly AI Search platforms answer branded MongoDB queries
- Partner integrations live and in-market across priority agent frameworks
- Cross-functional commitments secured against the agent-first roadmap, with progress tracked and reported quarterly
- Pipeline sourced or influenced from the agentic builder segment
- New customer acquisition and free-to-paid conversion originating from the agentic builder segment via PLG motion
Our take
MongoDB is an open-source, cross-platform, document-oriented database system. It stores data as JSON-like documents and is written in C++, Go, JavaScript and Python.
Essentially, the company develops tools and blueprints to help businesses and organizations modernize their legacy applications, migrating them to the MongoDB database and the MongoDB Atlas cloud database. With this initiative, MongoDB is particularly taking aim at Oracle customers with ageing applications running on the Oracle relational database system.
Since its release, MongoDB has become one of the most popularly used NoSQL database systems due to its ease of use and efficiency. It is also the fastest-growing database ecosystem, and boasts hundreds of millions of downloads. Recently, the company announced a partnership with Patronus AI, an automated evaluation and security platform, through which it will bring automated LLM evaluation and testing capabilities to enterprise customers.
Freddie
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Funding (last 2 of 8 rounds)
Jan 2015
$80m
SERIES G
Oct 2013
$150m
SERIES F
Total funding: $311.1m
Company HQ
Theater District, New York, NY
Leadership
Dwight Merriman
(Co-Founder)Previously CTO and Co-founder at DoubleClick for 10 years, and Chairman at AlleyCorp for 15 years.
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