Machine Learning Intern

📋 Description

• Develop reward models to improve aesthetics, motion quality, temporal consistency, and prompt
• Study how base-model behavior affects post-training outcomes and use experimental findings to
• Design rigorous evaluations and conduct large-scale experiments on generative video models
• Build systems for ingesting, preprocessing, curating, and delivering large-scale video datasets
• Develop distributed pipelines for dataset generation, deduplication, preprocessing, and repeated
• Improve the reliability, reproducibility, and efficiency of data and model-training workflows

🎯 Requirements

• Pursuing a bachelor’s or master’s degree in computer science, engineering, machine learning, or a
• Experience building data pipelines, ML infrastructure, or distributed systems through research
• Familiarity with tools such as Ray, PySpark, Airflow, Docker, Kubernetes, or equivalent technologies
• Experience with cloud storage or compute platforms such as AWS, Google Cloud, or Azure
• Understanding of practical considerations around data throughput, storage layout, caching
• Proficiency in Python and interest in building reliable systems for large-scale machine learning

🎁 Benefits

• A defined project and named senior mentor before your first day
• Weekly one-on-one meetings and clear project milestones
• A meaningful compute allocation for your research
• The opportunity to own a complete research or engineering result
• First-author positioning by default where your contribution supports a publication
• Timely internal review of research intended for submission