Senior Research Engineer (Code World Models)

JetBrains is a global software company that creates intelligent tools for software developers and teams. Since 2000, we have built products that help developers work more productively, write higher-quality code, and stay focused on solving real problems.

The JetBrains Research team is looking for a Senior Research Engineer to work on Code World Models: models that learn how software systems behave, change, execute, and interact with developer tools.

This role is focused on model pre-training and mid-training for code-centric foundation models. You will work on data, training pipelines, evaluation, and experiments that improve how models understand programs, repositories, execution, tests, and software engineering workflows.

• Design and run pre-training, continued pre-training, and mid-training experiments for code models.
• Build and improve data pipelines for large-scale model training, including filtering, deduplication, mixture design, and dataset quality checks.
• Work with code corpora, repositories, tests, execution traces, and synthetic data.
• Develop evaluations for complex repository-level code reasoning tasks.
• Collaborate with researchers and engineers working on ML for code and AI developer tools.

• Have hands-on experience with model pre-training, continued training, or mid-training.
• Have strong engineering skills in Python and experience with modern ML frameworks.
• Understand large-scale ML training workflows, including data processing, distributed training, checkpointing, evaluation, experiment tracking, and debugging.
• Have experience working with large datasets and care about data quality, contamination, sampling, and reproducibility.
• Have a background in NLP, ML for software engineering, or a similar domain.
• Enjoy working on research problems with high uncertainty and turning ideas into working experiments.

• Have experience training or adapting models for code generation, code understanding, software agents, program repair, test generation, or repository-level reasoning.
• Have worked with execution-based data, such as unit tests, traces, logs, compiler feedback, runtime states, or sandboxed code execution.
• Have experience with large-scale distributed training of models with 70B+ parameters.
• Understand evaluation challenges for code models, including benchmark contamination, flaky tests, execution-based scoring, and long-horizon task evaluation.
• Have contributed to ML infrastructure, open-source projects, or research systems.

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