Teams at JetBrains

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.

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.

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.