Research Engineer – Agentic Models

🕒 January 16

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JetBrains

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2000

🤝 B2B

☁️ SaaS

🤖 Artificial Intelligence

B2B • SaaS • Artificial Intelligence

JetBrains is a software company that builds professional developer tools and integrated development environments (IDEs) — including IntelliJ IDEA, PyCharm, WebStorm, Rider, and others — plus team and CI/CD tools like TeamCity, YouTrack, Datalore, and code-quality services. The company also offers AI-powered developer features (Junie, AI Assistant, AI Enterprise), a marketplace for plugins, educational offerings (JetBrains Academy, courses, free licenses for students/teachers), and enterprise services for managing developer tooling at scale. JetBrains focuses on improving developer productivity, collaboration, and code quality for individual developers and organizations worldwide.

📋 Description

• 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.

🎯 Requirements

• 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. • 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.

🏖️ Benefits

• Health insurance • Paid time off • Flexible working arrangements • Professional development