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Research Engineer (Agentic Models) at JetBrains. . Location: Amsterdam, Netherlands; Belgrade, Serbia; 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’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.. . We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.. . You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.. . As part of our team, you will:. . . Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.. . Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.. . Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.. . Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.. . Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.. . Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.. . Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. . . . We’ll be happy to bring you on board if you have:. . . Hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.. . Experience with a modern deep learning framework, such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).. . A solid understanding of LLM training basics – tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.. . The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.. . A product-aware mindset – you care about how agents are actually used by developers and can translate product needs and failure modes into modeling and evaluation work.. . At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.. . . Our ideal candidate would have experience with:. . . ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.. . Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.. . Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.. . AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.. . Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.. . Inference optimization and serving optimized models in production.. . . #LI-KP1. We process the data provided in your job application in accordance with the . Recruitment Privacy Policy.