Senior Product Manager (JetBrains Context)

Coding agents are only as good as the context they're given. Most explore a repository like a new hire on day one – grepping, opening files, guessing. JetBrains Context replaces that with actual knowledge of your whole codebase and resources, served to any agent that asks. If you want to own the layer that decides what an agent knows before it writes a single line, this is for you.

As agents get better at searching for themselves, what is the durable value of a managed retrieval layer? We need someone with an opinionated answer to own JetBrains Context as a product – defining what it is for, who it is for, how we measure success, and where it goes next.

This is a hands-on role where you'll manage the product from a terminal and agent session rather than slides. You'll bridge the technical core and cross-functional teams: collaborating closely with engineering and ML teams on indexing pipelines and retrieval quality, working with Air, IDE, and Junie teams on integrations, and partnering with marketing, developer advocacy, sales, and customer success on positioning and adoption. You'll take full ownership of defining problem statements, setting explicit trade-offs, and balancing cost, latency, and freshness across the platform.

Day to day, you will:

• Build and maintain a deep working understanding of chunking, embeddings, index freshness, retrieval, reranking, and agent-facing surfaces.
• Define and defend an opinionated vision for the product's evolution, including explicit target audiences, use cases, and key success metrics.
• Translate product strategy into actionable problem statements, priorities, plans, and milestones with an explicit definition of “done”.
• Communicate product direction clearly to engineering, leadership, cross-functional stakeholders, customers, and prospects.
• Analyze competing context and code-search products through direct use to establish clear differentiators and identify gaps.
• Define core operational and product metrics – index freshness, retrieval quality, agent task outcomes, adoption, retention, and unit economics – to guide decision-making.
• Represent the product externally by sharing roadmaps, publishing updates, speaking to developer audiences, and gathering community feedback.
• Partner with ML teams on model evaluation and platform teams on service scale, reliability, and cost.