Senior Product Manager (AI Assistant for IDEs)

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

JetBrains AI Assistant has quickly become a core part of how developers work inside our IDEs. We remain steadfast in our ambitious vision to create an assistant that supports the entire development life cycle across JetBrains IDEs. We are looking for a Senior Product Manager to join the AI Assistant team and support these efforts. You will own key scenarios and take control of them from end to end, increase product adoption and satisfaction, and keep us ahead as the market shifts from AI plugins to AI-powered IDEs.

In this role, you will: 

• Define, prioritize, and deliver the highest-impact developer scenarios (code generation, refactoring, debugging, test authoring, docs, reviews) across JetBrains IDEs.

• Build and maintain a transparent roadmap, balancing short-term wins and long-term bets for AI workflows in IDEs.

• Conduct R&D, working daily with developers, ML/AI researchers and engineers, analysts, designers, technical writers, developer relations specialists, and PMMs to ship fast and learn faster.

• Engage in research, running continuous discovery (interviews, diary studies, telemetry, AI evaluations), shaping problem statements, prototyping, and validating solutions.

• Translate model capabilities and constraints (latency, cost, context limits, hallucinations, privacy) into product decisions and UX trade-offs that drive product excellence.

• Define OKRs, success metrics, and A/B tests, improving activation, feature discoverability, and long-term retention.

• Create distinct yet coherent experiences across all the IntelliJ-based IDEs.

• Partner with PMMs on go-to-market efforts, creating crisp positioning, messaging, demos, and launch plans. Tighten the onboarding process to increase trial-to-license conversion.

• Craft end-to-end demos that mirror real-world developer workflows, avoiding simplified or contrived examples.

• Improve reliability through grounding, context, and explanations, minimize failures or hallucinations, and uphold privacy-by-design and enterprise constraints.

• Use conventional and unconventional methods to generate, prioritize, and test hypotheses rapidly.

We’d love to have you on our team if you:

• Have at least 5 years of experience in product management (or equivalent) and shipping end-user products. Experience with developer tools and/or AI would be a strong plus.

• Have enough technical knowledge to collaborate with engineers (e.g. prior SWE experience, a computer science degree, or the equivalent).

• Have a firm grasp of AI tooling and the developer ecosystem (IDE workflows, code review, CI/CD, repositories, package managers).

• Are comfortable with AI concepts, including prompting, agents, retrieval, context, evaluation frameworks, latency, cost trade-offs, safety, and guardrails.

• Possess excellent UX instincts for complex technical tasks in IDEs.

• Are highly analytical with the ability to define metrics and OKRs, run experiments, and interpret telemetry – with SQL or Python knowledge being a plus.

• Can communicate clearly in English (both written and verbal) and explain complex technical decisions simply.

• Can work well with others and drive alignment across research, engineering, marketing, and executive stakeholders.

We’d be especially thrilled if you have:

• Hands-on development experience in the last 6 years.

• A track record of improving onboarding, activation, and upselling technical products.

• Experience with AI evaluations (golden sets, rubric-based scoring), agents, and multi-tool orchestration.

• Familiarity with enterprise requirements (privacy, on-prem, regulated environments).

• An active presence in the developer community (talks, OSS, writing).

How we measure success in your first 6–12 months:

• Adoption and retention: Growth in penetration rate, WAU/MAU for AI features, feature stickiness, and return-to-use rate at D7, D30, and D90.

• Quality and trust: Higher CSAT, suggestion acceptance rates, assisted-task completion rates, and helpfulness scores, as well as lower error/rollback rate and hallucination incident rates.

• Business impact: Measurable increase in trial-to-license conversion from AI features.

• Velocity: Consistent delivery of hypotheses each quarter and validated learning every month.

How we work:

• We practice continuous discovery and delivery with heavy dogfooding in JetBrains IDEs.

• We roll out changes safely and incrementally using feature flags, canaries, and controlled experiments.

• We work in close partnership with PMMs on launches and life cycle campaigns.

• We maintain a relentless focus on privacy, reliability, and developer-grade UX.