Research Engineer (Agentic Models)

JetBrains; Madrid; Presencial; EUR 60.000 - 80.000; Jornada completa

Descripción de la vacante

A technology company in Madrid seeks a Research Engineer to develop AI-powered coding agents. The role involves designing and implementing training pipelines for LLMs, collaborating with various teams, and improving model architectures. Ideal candidates will have hands-on experience with LLM training, expertise in deep learning frameworks like PyTorch, and strong Python skills. This position offers a unique opportunity to work at the intersection of AI and software development.

Formación

• Extensive experience training LLMs in research or production settings.
• Deep expertise in modern deep learning frameworks like PyTorch.
• Strong understanding of LLM fundamentals including architectures and tokenization.

Responsabilidades

• Design and implement training pipelines for coding agents.
• Train LLMs for agent workflows within JetBrains IDEs.
• Collaborate with teams to transform product visions into models and features.

Conocimientos

Training LLMs

Deep learning frameworks (PyTorch)

Python programming

Distributed training

Model evaluation

Herramientas

Kubeflow

MLflow

Weights & Biases

Descripción del empleo

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.

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.

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.

As Part Of Our Team, You Will

• 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.

We’ll be happy to bring you on board if you have:

• Extensive hands‑on experience training LLMs (pre‑training, fine‑tuning, or post‑training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, 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 developers actually use agents 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.

Our Ideal Candidate Would Have Experience With

• 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.

We are an equal opportunity employer

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.

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