AI Engineer Internship – LLM Data

The Role

As an AI Engineer Intern specializing in LLM data, you will work with our team to build and improve high-quality training data for foundation models across pre-training, mid-training, and post-training. You will gain hands-on experience with large-scale data processing, LLM-based data synthesis, data quality evaluation, and model experimentation.

Responsibilities

• Build, curate, and improve datasets for LLM pre-training, mid-training, and post-training, including time-sensitive requests that may require fast turnaround.

• Develop and improve data processing workflows for data extraction, cleaning, filtering, deduplication, transformation, and quality control.

• Use LLMs to generate, refine, filter, and evaluate synthetic data.

• Analyze data quality and identify issues such as duplication, low-quality samples, distribution gaps, and coverage limitations.

• Run experiments to understand how different data sources and processing strategies impact model performance.

• Support model inference, evaluation, and training experiments when needed to validate data quality.

• Research new datasets, data processing techniques, and LLM data methodologies.

• Collaborate closely with researchers and engineers on fast-moving foundation model projects.

Qualifications

• Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.

• Strong programming skills in Python.

• Good understanding of machine learning, deep learning, NLP, or large language models.

• Hands-on experience with LLMs through research, coursework, projects, internships, or open-source work.

• Comfortable working with datasets and performing data processing and analysis.

• Strong problem-solving skills and willingness to learn new technologies quickly.

• Ability to work independently and collaborate effectively in a fast-paced research environment.

Preferred Qualifications

• Experience with PyTorch, Hugging Face, vLLM, or similar ML/LLM frameworks.

• Experience with LLM data synthesis, fine-tuning, model evaluation, or prompt-based generation.

• Familiarity with LLM training pipelines, including pre-training, supervised fine-tuning, or other post-training methods.

• Experience working with large-scale datasets or distributed data processing.

• Relevant research, open-source contributions, competitions, or personal projects in LLMs or generative AI.