ML Solutions Architect (Data Agents) Amsterdam, Netherlands; Berlin, Germany; Limassol, Cyprus;[...]

ML Lead (Context Engineering, Data agents, Evaluation)

Amsterdam, Netherlands; Berlin, Germany; Limassol, Cyprus; London, United Kingdom; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the strongest, most effective developer tools on earth.

We believe that the data domain will soon undergo the same kind of disruption that is currently happening in software engineering. Agents will become increasingly capable of solving tasks related to analytics, working with company data, and data engineering. At the same time, the key to successfully solving applied problems will lie in context, while the agents themselves will become commoditized.

That’s why we’ve started building a product that enables semi-automated context collection and generation for a company’s existing data, metrics, processes, and domain knowledge. This context will empower data agents to perform better within specific company environments.

We’re looking for a researcher to lead our work, focusing on assembling, evaluating, and optimizing the relevance and usefulness of context for agents.

If you're passionate about democratizing data and transforming how businesses operate in the new agent era, we want you on our team!

In this role, you will:

• Lead the development of an ML/AI layer for automated context generation.
• Design methods to extract insights from metrics, logs, documents, and internal tools.
• Define and implement evaluation metrics for context quality and agent performance.
• Collaborate with product and engineering teams to integrate context into workflows.
• Mentor ML researchers and engineers.
• Guide experimentation and prototyping.
• Share progress with stakeholders and represent the team externally.

We will be happy to have you on our team if you have:

• At least 5 years of experience building ML- and LLM-based applications.
• A solid understanding of LLM architectures, limitations, and modern capabilities.
• Hands‑on experience with agent‑based systems and retrieval‑augmented generation.
• Experience in implementing algorithms from academic papers.
• Clear and effective communication skills in English.

It would be great if you have:

• Experience working with company‑internal data, metrics, or BI tools.
• Experience with evaluating LLM output quality in real‑world applications.
• Exposure to human‑in‑the‑loop systems or feedback‑driven model improvement.
• A background in information retrieval or knowledge engineering.
• Contributions to open‑source projects in the LLM or data tooling space.

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