Key Responsibilities

• Conduct cutting-edge research in learning from narration to detect key points or evaluate proficiency in procedural video understanding.
• Design and implement novel algorithms for aligning video and text representations or training multi-modal learning using open source language models.
• Perform literature review, formulate hypotheses, run experiments, and analyze results.
• Lead or contribute to research paper writing, including potential submission to top-tier computer vision or machine learning conferences (e.g., CVPR, ICCV, NeurIPS, ECCV).
• Write well-structured, efficient code using deep learning frameworks such as PyTorch.    

Minimum Qualifications

• Currently enrolled in a PhD program in Computer Vision, Machine Learning, Artificial Intelligence, or a closely related field.
• Publication record in top-tier conferences (e.g., CVPR, ICCV, ECCV, WACV, NeurIPS, ICLR).
• Prior experience with multimodal language models (i.e, Q formers, LoRA, and LLMs), OR vision-language representation alignment (e.g., CLIP).
• Excellent programming skills,  ability to write reproducible research code, and proficiency in deep learning frameworks, especially PyTorch.
• Strong written and verbal communication skills.
• Ability to independently drive research, from ideation to experimentation and publication.

Bonus Qualifications

• Familiarity with procedural video downstream tasks such as  proficiency estimation or action segmentation/detection, multimodal learning, and video understanding.