Software Engineer, Computer Vision - Video AI

Meta Meta · Big Tech · Bellevue, WA +2

Software Engineer focused on Computer Vision and Video AI at Meta, developing advanced video solutions for products like Messenger and AR/VR. The role involves researching and developing AI/ML-based compression algorithms to reduce compute footprint and improve user experience, optimizing video codec efficiency, and defining the video optimization roadmap. Requires experience in computer vision, video codecs, and neural compression techniques.

What you'd actually do

  1. Research, develop, and troubleshoot real time communication systems, related to video and audio codecs, cameras, displays, and microphones
  2. Designing AI/ML-based compression algorithms to reduce the platform's compute footprint, while improving end-user experience
  3. Optimize and improve video codec efficiency, encode rate control, processing speed, video pre/post-processing, and error resilience
  4. Debug and diagnose quality of end-to-end video experience on lossy networks in real-time communication scenarios
  5. Define the video optimization roadmap for both low-end and high-end networks and devices

Skills

Required

  • Computer Science, Computer Engineering, or relevant technical field
  • 5+ years software development experience or PhD with 2+ years
  • 3+ years of experience in computer vision, video/image codecs, video/image processing or a relevant domain
  • Experience in neural video/image compression and AI research
  • Experience with computer vision, video/image codecs, or video/image processing
  • Experience in C/C++ multithreaded programming
  • Experience designing power-efficient software for mobile or embedded systems

Nice to have

  • Experience leading large or complex projects
  • Knowledge of multimedia stack, including containers, codecs, and AV synchronization
  • Experience developing algorithms to improve video quality for calling and video conferencing
  • Industry experience in Video-on-Demand, RTC, or videos at scale

What the JD emphasized

  • Experience in neural video/image compression and AI research
  • Leveraging AI-based enhancement tools that complement standard video-codecs (AV1)
  • Using ML-based quality metrics and metadata for large-scale quality evaluation
  • Full end-to-end neural-compression techniques at scale

Other signals

  • AI/ML-based compression algorithms
  • neural video/image compression
  • ML-based quality metrics