Senior Product Manager (JetBrains Context)

JetBrains is a company that creates intelligent software development tools used by more than 15 million users and 300,000 companies, including 88 of the Fortune Global Top 100. The role is focused on the JetBrains Context product, which manages knowledge of codebases for coding agents.

• Well-argued point of view on AI-first development and context layers for agents.
• Comfort with technical depth across embeddings, retrieval quality, sub-agents, tool-calling, indexing costs, and API design.
• Habit of using the product and competitors' tools daily.
• Excellent communication skills adapted to diverse audiences.
• Data-informed approach combining telemetry, evaluation, customer conversations, and market signals.
• Comfort with ambiguity and defining new categories.
• Proven experience owning complex developer, platform, or infrastructure products end to end.
• Hands-on familiarity with modern AI coding tools and agent workflows.
• Technical capability to work directly with engineering on retrieval behavior, APIs, data models, and integrations.
• Ability to define metrics and build evaluation habits for quality-sensitive products.
• Experience communicating product externally via written content, talks, or community engagement.
• Practical experience with search, retrieval, RAG, developer search, or static analysis at scale is a plus.

• Build and maintain a deep working understanding of chunking, embeddings, index freshness, retrieval, reranking, and agent-facing surfaces.
• Define and defend a vision for the product's evolution, including target audiences, use cases, and success metrics.
• Translate product strategy into actionable problem statements, priorities, plans, and milestones.
• Communicate product direction clearly to engineering, leadership, stakeholders, customers, and prospects.
• Analyze competing context and code-search products to establish differentiators and identify gaps.
• Define core operational and product metrics to guide decision-making.
• Represent the product externally through roadmaps, updates, speaking engagements, and community feedback.
• Partner with ML teams on model evaluation and platform teams on service scale, reliability, and cost.

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