AI Research Scientist, Computer Vision - Video Intelligence

Meta

$130K — $180K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science or related field, or equivalent experience.
  • 2+ years in computer vision research focusing on deep learning for video understanding.
  • Proficiency with deep learning frameworks like PyTorch or TensorFlow.
  • Experience with large-scale visual datasets and their preprocessing.
  • Background in designing and validating deep learning models through rigorous experimentation.

Responsibilities

  • Design and implement computer vision models for video understanding tasks.
  • Develop multimodal learning approaches integrating visual, audio, and textual signals.
  • Build and benchmark video representation learning methods on large-scale datasets.
  • Run controlled experiments to test hypotheses and analyze results.
  • Collaborate with teams to translate research into production-ready features on Facebook.
  • Contribute to designing large-scale video datasets and annotation pipelines.
  • Write automated tests and monitor deployed models for performance and reliability.

Benefits

  • Opportunities for collaboration with a multidisciplinary research team.
  • Exposure to cutting-edge technology and methodologies in AI and computer vision.
  • Access to a vast range of large-scale datasets for research purposes.
  • Potential for publishing research in top-tier peer-reviewed venues.
  • Support for continuous learning and professional growth.
Full Job Description
Meta is seeking an AI Research Scientist to advance computer vision research applied to Facebook Video Intelligence. In this role, you will develop and apply state-of-the-art deep learning and computer vision techniques to understand, analyze, and generate insights from large-scale video content across Meta's platforms. You will work alongside a multidisciplinary team of researchers and engineers to push the boundaries of video understanding, including temporal modeling, action recognition, scene understanding, and multimodal learning, directly shaping how billions of people experience video on Facebook.

Responsibilities

Design and implement novel computer vision and deep learning models for large-scale video understanding tasks including action recognition, temporal segmentation, and scene classification
• Develop and evaluate multimodal learning approaches that combine visual, audio, and textual signals from video content
• Build and benchmark video representation learning methods using self-supervised and weakly supervised techniques on large-scale datasets
• Run controlled experiments to test research hypotheses, analyze results, and make data-driven decisions to guide model development
• Collaborate with engineering and product teams to translate research advances into production-ready video intelligence features on Facebook
• Contribute to the design and curation of large-scale video datasets and annotation pipelines to support model training and evaluation
• Write automated tests and implement monitoring to ensure reliability and performance of deployed video understanding models
• Communicate research findings through internal technical documentation, presentations, and contributions to peer-reviewed publications
• Participate in code reviews to maintain code quality and share knowledge of computer vision best practices with peers
• Identify and address privacy, security, and integrity considerations throughout the video intelligence research and development lifecycle

Minimum Qualifications
• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• 2+ years of experience in computer vision research, including deep learning approaches applied to video understanding, image recognition, or related visual domains
• Experience designing, training, and evaluating deep learning models using frameworks such as PyTorch or TensorFlow for video or image tasks
• Experience working with large-scale datasets, including data preprocessing, augmentation, and evaluation pipelines for visual data
• Experience implementing and validating research ideas through rigorous experimentation, including ablation studies and quantitative benchmarking
• Experience communicating technical research findings in written form, such as technical reports, research papers, or design documents

Preferred Qualifications
• Experience applying self-supervised or weakly supervised learning techniques to large-scale unlabeled video corpora
• Experience collaborating with cross-functional teams to integrate research models into production systems at scale
• Research experience in temporal video modeling, optical flow, video generation, or multimodal video-language learning
• Proven track record of contributions to peer-reviewed venues such as CVPR, ICCV, ECCV, NeurIPS, ICML, or similar conferences

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