Research Engineer (Agentic Models)
11 days ago
Madrid
ppAt 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. /p pWe’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. /p pYou’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. /p h3As Part Of Our Team, You Will /h3 ul liDesign, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents. /li liTrain and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs. /li liBuild and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks. /li liDesign evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design. /li liAnalyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets. /li liWork with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets. /li liCollaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. /li /ul pWe’ll be happy to bring you on board if you have: /p ul liExtensive hands‑on experience training LLMs (pre‑training, fine‑tuning, or post‑training) in a research or production setting. /li liDeep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar). /li liStrong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs. /li liThe 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. /li liA product‑aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work. /li liAt least 3 years of Python experience writing clean, maintainable code in modern ML codebases. /li /ul h3Our Ideal Candidate Would Have Experience With /h3 ul liML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM. /li liLarge‑scale data and training pipelines, e.g. MapReduce‑style clusters, multi‑node GPU training, or workloads on the order of 1M+ CPU/GPU hours. /li liDesigning and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks. /li liAI agent development, such as tool‑using agents, planners, or multi‑step coding workflows, and familiarity with agentic frameworks or patterns. /li liExperiment tracking and observability using tools like Weights Biases, MLflow, Langfuse, or similar. /li liInference optimization and serving optimized models in production. /li /ul h3We are an equal opportunity employer /h3 pWe know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation. /p pWe process the data provided in your job application in accordance with the Recruitment Privacy Policy. /p /p #J-18808-Ljbffr
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