Jobs at JetBrains

Design, improve, and execute test scenarios for Kotlin-based web applications; perform exploratory testing; investigate and reproduce complex user issues; review requirements; communicate with teams to find optimal solutions and maintain high-quality sales and licensing services.

Drive Hexana from MVP to scalable product by researching advanced user workflows, defining roadmaps, validating hypotheses, tracking competitor tooling, shaping monetization and go-to-market, and communicating strategy to stakeholders.

Partner with engineering teams to drive Kotlin backend adoption: guide migrations from Java, troubleshoot integration and scaling, recommend tailored strategies, track adoption stages, identify and remove blockers, and maintain feedback loops while running workshops, demos, and outreach.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.

Create clear, structured documentation for data, metrics, tools, and services (data catalogs, API/SDK docs, user guides, UI text). Own documentation area, lead projects, improve processes, support contributors, and mentor junior writers to raise documentation quality across the Data Office.

Own the end-to-end Kotlin-in-IDE experience for the IntelliJ Kotlin plugin: define vision and roadmap, dogfood and prototype features (including AI agents), maintain reference projects, run user interviews, coordinate with compiler and IDE release cycles, and represent the product externally.

Manage end-to-end email campaigns: develop, execute, A/B test, analyze performance, and optimize messaging. Collaborate with cross-functional teams, support AI tool marketing, and scale campaigns across languages using marketing automation.

Support engineering teams adopting Kotlin Multiplatform from evaluation to production: advise on migration, integration, architecture, performance (build) issues, gather feedback, advocate product improvements, and collaborate with product and engineering to drive KMP roadmap and adoption.

Join the TeamCity product team as a Senior Product Analyst embedded in product and Data Analytics. Perform ad hoc analyses, build dashboards, define event logging and metrics, evaluate releases, conduct research analytics, and design ETL pipelines and data models to enable end-to-end product analytics.

Develop and maintain the JetBrains Runtime to ensure IDEs run fast and render well. Provide low-level support for UI frameworks (Swing, Compose for Desktop), work on OpenJDK projects, implement hardware-accelerated rendering (Metal/Vulkan/OpenGL/D3D), fix JBR issues, and optimize/refactor cross-platform system UI code for Windows, macOS, and Unix.

Design, build, and lead development of AppGlass features; develop AI-first tooling and automation; implement enterprise integrations and deployments across Kubernetes and cloud; and improve reliability, testing, performance, and security for production use.

Work directly with engineering teams to drive Kotlin adoption on the backend: track adoption stages, design migration strategies, guide integration and scaling, troubleshoot issues, document blockers, and run workshops, demos, and proactive outreach to move teams into production use.

Manage the full lifecycle of email campaigns: develop, execute, optimize, and A/B test emails; collaborate with product, brand, sales, localization, and design; analyze performance metrics; use marketing automation to scale campaigns across languages and regions; support AI tool marketing initiatives.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Lead the product vision and roadmap for the IntelliJ Scala plugin by understanding developer workflows, dogfooding features, running user research and metrics, coordinating with engineering and cross-functional teams, building reference projects, and delivering improvements in build systems, debugging, code insight, and tooling integration.

Design and prototype ML solutions to improve developer workflows (code completion, generation, agents, test generation). Apply or train open-source models, build reproducible training and evaluation pipelines, stay current with ML-for-code research, collaborate with colleagues, and provide mentorship.

Develop and maintain SFT and RL post-training pipelines for multi-step coding agents, train and adapt LLMs for agent workflows, build evaluation and simulation environments, design metrics and evaluation frameworks, analyze results to improve models and datasets, and collaborate with research, product, and infra teams to ship models into JetBrains IDEs.

Ship AI product features combining engineering and ML; build and improve agent workflows; optimize latency, cost, and reliability; track AI research and turn it into product; build self-improving loops that run experiments and learn from feedback.

Design, train, and deploy large language models from scratch; convert business requirements into technical specs; collect and process pretraining/fine-tuning datasets; run distributed training on large GPU clusters; support and improve production ML subsystems and tooling.

Develop and maintain foundational Kotlin core libraries used by many developers; contribute to language features, tools, and specifications; participate on a design committee; become domain expert in CS topics; write design documents; focus on API design and performance engineering.