ai engineer

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

• 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 where appropriate;
• Design and improve the agent loop itself, 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 both hosted frontier APIs and self-hosted or open-weight models, making pragmatic decisions about where each model belongs 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 where strong judgment is needed rather than a prewritten playbook.

• 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, whether via prompting, context engineering, fine-tuning, preference optimization, or broader 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 from 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 the ability to align engineering and product decisions.

• Strong base salary with competitive pay reflecting skills and experience;
• Flexible work location with the freedom to work from home or from the office;
• Remote work option to spend up to 30 days per year working remotely from abroad;
• Extra time off;
• Medical insurance allowance for employee and family;
• Learning and development opportunities including access to conferences, courses, and language classes;
• Relocation support;
• Language classes for the local language or English skills;
• Hot meal or lunch allowance on workdays;
• Mental health support with easy access to professional services;
• Sports benefit including an on-site gym or sports club stipend;
• Internal events such as company-wide celebrations and team gatherings.