AI Research Scientist, Computer Vision - Video Intelligence

Meta Meta · Big Tech · Menlo Park, CA

AI Research Scientist focused on computer vision for video intelligence at Meta. The role involves developing and applying deep learning models for video understanding (action recognition, scene understanding), multimodal learning (visual, audio, text), and representation learning. Responsibilities include designing experiments, collaborating with engineering/product teams, contributing to datasets, and publishing research findings. Requires a degree in a relevant field and experience in computer vision research, deep learning, large-scale datasets, and experimentation, with a proven publication record.

What you'd actually do

  1. Design and implement novel computer vision and deep learning models for large-scale video understanding tasks including action recognition, temporal segmentation, and scene classification
  2. Develop and evaluate multimodal learning approaches that combine visual, audio, and textual signals from video content
  3. Build and benchmark video representation learning methods using self-supervised and weakly supervised techniques on large-scale datasets
  4. Run controlled experiments to test research hypotheses, analyze results, and make data-driven decisions to guide model development
  5. Collaborate with engineering and product teams to translate research advances into production-ready video intelligence features on Facebook

Skills

Required

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 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
  • 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

Nice to have

  • Publications in top-tier computer vision or machine learning conferences (CVPR, ICCV, ECCV, NeurIPS, ICML, etc.)

What the JD emphasized

  • Proven track record of contributions to peer-reviewed venues such as CVPR, ICCV, ECCV, NeurIPS, ICML, or similar conferences

Other signals

  • advancing computer vision research
  • develop and apply state-of-the-art deep learning and computer vision techniques
  • push the boundaries of video understanding
  • multimodal learning
  • translate research advances into production-ready video intelligence features