Staff/Senior AI Engineer, AI for Code

JetBrains builds developer tools used by millions of engineers. The AI for Code team develops next-generation coding agents and agentic workflows that understand codebases, plan and execute multi-step tasks, collaborate with developers, and deliver reliable results within real development environments.

• Strong software engineering fundamentals and a track record of shipping complex systems to production.
• Hands-on experience building LLM-powered products, coding agents, or other AI systems.
• Experience improving model behavior through systematic iteration such as prompting, context engineering, fine-tuning, preference optimization, or other post-training methods.
• Practical experience with evaluation and benchmarking for LLM systems, including defining task-grounded success metrics and catching regressions.
• Experience working from noisy real-world signals rather than only clean benchmark datasets.
• Good judgment about trade-offs between model quality, latency, reliability, privacy, and cost.
• Confidence working with ambiguity and taking ownership of a direction over multiple iterations.
• Strong communication skills and ability to align engineering and product decisions.

• Build production-ready coding agents and agentic workflows for real developer tasks inside JetBrains products.
• Turn promising model capabilities into dependable product behavior through prompt design, context construction, fine-tuning, instruction-tuning, or other post-training techniques.
• Design and improve the agent loop including tool use, execution strategy, safeguards, and task completion quality.
• Create evaluation suites and quality infrastructure for agent behavior, including online and offline evaluations, regression checks, failure analysis, and release criteria.
• Build feedback loops from real usage using logs, user signals, and edge cases to improve data, evaluations, and agent behavior.
• Work with hosted frontier APIs and self-hosted or open-weight models, making pragmatic decisions based on capability, latency, reliability, privacy, and cost.
• Collaborate closely with product managers, software engineers, ML engineers, and researchers to ship features end to end.
• Help define the technical direction for future work, especially in ambiguous areas requiring strong judgment.

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