ai engineer

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

• Build production-ready coding agents and agentic workflows for real developer tasks inside JetBrains products;
• Turn model capabilities into dependable product behavior through prompt design, context construction, fine-tuning, instruction-tuning, and other post-training techniques;
• Design and improve agent loops, including tool use, execution strategy, safeguards, and task completion quality;
• Create evaluation suites and quality infrastructure, including online and offline evaluations, regression checks, failure analysis, and release criteria;
• Build feedback loops from real usage, 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 decisions based on capability, latency, reliability, privacy, and cost;
• Collaborate with product managers, software engineers, ML engineers, and researchers to ship features end to end;
• Help define the technical direction for future work in ambiguous areas;
• Ship one or more agent capabilities that users can rely on for meaningful work;
• Establish better evaluation coverage and clearer release criteria for agent behavior;
• Help build a repeatable loop from idea to shipped capability: prototype, evaluate, learn from usage, improve, and scale.

• 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 prompting, context engineering, fine-tuning, preference optimization, or broader post-training methods;
• Practical experience with evaluation and benchmarking for LLM systems, including task-grounded success metrics and regression detection;
• Experience working with 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 the ability to align engineering and product decisions.

• Competitive base salary reflecting skills and experience;
• Flexible work location with the option to work from home or from the office;
• Up to 30 days per year working remotely from abroad;
• Extra time off;
• Medical insurance allowance for the employee and family;
• Access to conferences, courses, and language classes;
• Relocation support is available;
• Hot meals or a lunch allowance on workdays;
• Mental health support;
• On-site gym or sports club stipend;
• Internal company events;
• Benefits may vary depending on location.