> Markdown version of [/jobs/ext/37685-staff-senior-ai-engineer-ai-for-code](https://www.wearedevelopers.com/jobs/ext/37685-staff-senior-ai-engineer-ai-for-code). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff/Senior AI Engineer, AI for Code - **Company:** JetBrains - **Location:** Amsterdam, Netherlands (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, JetBrains, Large Language Models, Low Latency - **Published:** May 22, 2026 - **Apply:** https://nl.indeed.com/viewjob?jk=7147c8884b9082eb ## About the Role Do you have experience in Communication skills?, Do you have a Master's degree?, * 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. ## Description * 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 we need strong judgment rather than a prewritten playbook., * You ship one or more agent capabilities that users can rely on for meaningful work, not just demos. * You establish better evaluation coverage and clearer release criteria for agent behavior. * You help the team build a repeatable loop from idea to shipped capability: prototype, evaluate, learn from usage, improve, and scale. ## Related Videos - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Building a Friendly Kotlin SDK to Connect to JetBrains Space](https://www.wearedevelopers.com/videos/128-building-a-friendly-kotlin-sdk-to-connect-to-jetbrains-space) - [Engineering Mindset in the Age of AI - Gunnar Grosch, AWS](https://www.wearedevelopers.com/videos/1735-engineering-mindset-in-the-age-of-ai-gunnar-grosch-aws) - [Lies, Damned Lies and Large Language Models](https://www.wearedevelopers.com/videos/1231-lies-damned-lies-and-large-language-models) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai)