MongoDB is one of the most widely adopted database platforms in the world, used by tens of thousands of customers — including much of the Fortune 100 and a fast-growing set of AI-native startups. Our cloud platform, Atlas, runs across AWS, Google Cloud, and Azure.
MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform.
Responsibilities
Defining product strategy and roadmap for your area, aligned with MongoDB's broader AI strategy and business goals
Talking to customers — from AI-native startups to large enterprises — to understand how they build and deploy AI applications, where they struggle, and what MongoDB should do about it
Writing clear product narratives, memos, and specs that align stakeholders, clarify trade-offs, to guide conversations with engineering, design, product marketing, etc
Partnering with cross-functional stakeholders—including Engineering, Design, Product Marketing, Partners, Sales, and Developer Relations—to scope, prioritize, and ship, balancing experimentation with reliability and scale, and driving positioning, launches, and enablement
Identifying new opportunities autonomously—spotting gaps, shaping problem spaces, researching industry trends and running discovery sessions with customers
Define and own clear success metrics for your area and use them to prioritize, make trade-offs and communicate impact
You'll develop deep expertise in how companies build AI applications — including agentic workflows, retrieval and memory, evaluation, observability, and deployment patterns.
Requirements
5+ years in product management (or equivalent) building platforms, infrastructure, or developer-facing products — ideally in cloud, data, or AI
Proven end-to-end ownership of a product area: from framing the problem and vision through roadmap, execution, launch, and iteration
Customer obsession: you're comfortable running discovery calls, digging into workflows, and turning messy feedback into clear product decisions
Strong written and live communication: clear docs, productive meetings, confident stakeholder presentations. This matters more in an async, distributed team — writing is how you lead
Technical depth sufficient to collaborate with senior engineers on distributed systems, cloud services, and AI/ML tooling, and to ask good questions when something is unclear
Curiosity about AI: you follow how companies are building AI applications — agents, orchestration frameworks, RAG, fine-tuning, or other emerging patterns
Comfort with ambiguity: you help define the problem, the success metrics, and the path forward. In an early-stage team, this is the job
Conditions
Async-first. Documentation and written communication are how decisions travel across time zones
Sync when it makes sense. When we need to discuss something live, we hop on a call to unbundle issues together.
In-person when it matters. The team gets together periodically for planning, relationship-building, and the kind of work that's better done face to face