Research Engineer Intern - Environments, Data and Post-Training

About the role#

As a Research Engineer Intern, you will work at the intersection of engineering and applied AI research. You will contribute to post-training, RLVR, synthetic data generation, and large-scale evaluation workflows. Your work will help train language models to master tool use, agentic behavior, and real-world reasoning. You will shape rewards, run experiments, and build systems that directly improve model performance.

What you'll do#

• Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance.
• Design and run reward-shaping experiments and algorithmic improvements to improve LLM tool use, agentic behavior, and real-world reasoning.
• Quantify data usability, quality, and performance uplift on key benchmarks.
• Build and maintain data generation and augmentation pipelines that scale with training needs.
• Create and refine rubrics, evaluators, and scoring frameworks.
• Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
• Collaborate with AI researchers, applied AI teams, and experts producing training data.

What you'll need#

• Strong applied research background with a focus on post-training or model evaluation.
• Coding proficiency and hands-on experience working with machine learning models.
• Understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
• Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
• Ability to reason deeply about model behavior, experimental results, and data quality.
• Excitement to work in person in San Francisco five days a week.
• Pursuing a degree in Computer Science or Data Science.

Location & details#

• Location: San Francisco, California
• Modality: On-site, five days a week
• Type: Full-time internship
• Term: Rolling