AI Engineer Internship – LLM Data
Salary not listed • Abu Dhabi • Internship
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Internship
Categories
LLM Engineer
About the job
The Institute of Foundation Models is a research lab focused on building, understanding, using, and managing foundation models. We aim to advance AI research, train future AI leaders, and contribute to a knowledge-driven economy. 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.
- Develop and improve data processing workflows for 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 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.
Requirements
- 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.
- 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.