Applied Scientist, Prime Video

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Applied Scientist role at Amazon Prime Video focusing on leveraging NLP and Computer Vision research to enhance video content understanding and customer experience. The role involves leading research direction, creating roadmaps, and developing models for business applications, with an emphasis on delivering state-of-the-art digital video experiences.

What you'd actually do

  1. As a highly experienced and seasoned science leader, you will apply state of the art natural language processing and computer vision research to video centric digital media, while also responsible for creating and maintaining the best environment for applied science in order to recruit, retain and develop top talent.
  2. You will lead the research direction for a team of deeply talented applied scientists, creating the roadmaps for forward-looking research and communicate them effectively to senior leadership.
  3. You will also hire and develop applied scientists - growing the team to meet the evolving needs of our customers.

Skills

Required

  • building models for business application
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • programming in Java, C++, Python or related language
  • developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development
  • Have publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • state of the art natural language processing and computer vision research
  • deep learning algorithms, particularly with respect to computer vision algorithms

Other signals

  • end-to-end ownership of the product
  • apply state of the art natural language processing and computer vision research to video centric digital media
  • creating the roadmaps for forward-looking research
  • delight our customers by pushing the boundaries of content understanding and enrichment