About the role
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.
In this role, you will:
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.
We’ll be happy to have you on our team if you:
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.
It would be a plus if you:
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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