Research Engineer (Agentic Models) at JetBrains

JetBrains is 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 distributed GPU and MapReduce clusters to ship models into JetBrains products. Key Responsibilities: - 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 Required Experience & Skills: - 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) - Solid understanding of LLM training basics — tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs - At least 3 years of Python experience writing clean, maintainable code in modern ML codebases Preferred Qualifications: - 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 - ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM - Product-aware mindset — caring about how agents are actually used by developers