📋 Descripción
<p>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.</p>
<p>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.</p>
<p>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.</p>
<h3>As part of our team, you will:</h3>
<ul>
<li>Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.</li>
<li>Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.</li>
<li>Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.</li>
<li>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.</li>
<li>Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.</li>
<li>Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.</li>
<li>Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features.&nbsp;</li>
</ul>
<h3>We’ll be happy to bring you on board if you have:</h3>
<ul>
<li>Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.</li>
<li>Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).</li>
<li>Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.</li>
<li>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.</li>
<li>A 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>
<li>At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.</li>
</ul>
<h3>Our ideal candidate would have experience with:</h3>
<ul>
<li>ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.</li>
<li>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.</li>
<li>Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.</li>
<li>AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.</li>
<li>Experiment tracking and observability using tools like Weights &amp; Biases, MLflow, Langfuse, or similar.</li>
<li>Inference optimization and serving optimized models in production.</li>
</ul>
<p><span style="color: rgb(255, 255, 255);">#LI-KP1</span></p><div class="content-conclusion"><p><strong>We are an equal opportunity employer</strong><br><br>We 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>
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