Research Intern — Coding LLMs

We are looking for research interns to work on foundational areas for coding language models, including pre-training data, mid-training data, synthetic data generation, evaluation, and agentic coding.

Responsibilities

* Explore data-centric methods for improving coding LLMs, including data filtering, quality assessment, deduplication, data mixture, and diversity analysis.

* Build synthetic data and evaluation pipelines for code generation, code editing, repo-level reasoning, tool use, and multi-step coding tasks.

* Run experiments to analyze how data, model, and training strategies affect coding capabilities

* Work with large-scale code corpora, developer activity data, and agentic coding trajectories.

Requirements

• Currently pursuing a phd degree
• Strong programming skills in Python.
• Solid understanding of machine learning and large language models.
• Familiarity with LLM pre-training, mid-training, code models, data curation, evaluation, agents, or tool use.
• Strong experiment design, data analysis, and problem-solving skills.
• Interest in code intelligence, software engineering automation, and agentic coding.

Preferred Qualifications

• Experience with code data processing, GitHub-scale data, synthetic data, LLM evaluation, semantic deduplication, or agentic coding.
• Research experience, publications, or open-source projects in related areas are a

plus.

What We Offer

* Access to large-scale real-world coding data and agentic trajectories.

* Rich compute resources and model APIs for fast research iteration.

* Opportunities to work on real-world coding model applications and the full model development loop.