Research Engineer (Agentic Models)

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.
• Build and develop evaluation and simulation environments where coding agents can act.

Required 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.
• A solid understanding of LLM training basics – tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.

Bonus skills:

• 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.