Senior Research Engineer (Code World Models) (m/f/d)

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.