Machine Learning Scientist, Computer Vision and Scene Understanding, Prime Video

Key Skills

Algorithms, Analytical and Problem solving, C++, Design, Java Programming, Machine learning techniques, Python Programming, SQL, Statistics

Job Description

How often have you had an opportunity to be a founding member of a team solving significant customer problems through innovative AI technology at Amazon scale? Do you want to help invent the future and define the next generation of how customers watch videos? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? If yes, then you may be a great fit to join our Amazon Prime Video team.

Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals. Our team innovates, with many novel patents, inventions, and papers in the motion picture and television industry.

We are looking for passionate, hard-working, and talented individuals to help us push the envelope of scene understanding. We work on a broad array of research areas and applications, including but not limited to multimodal video understanding, unsupervised and self-supervised learning, 3D scene understanding, and large-scale image and video recognition and classification. Candidates should be prepared to help drive innovation in one or more areas of machine learning, computer vision, audio processing, and natural language understanding.

As a machine learning scientist on our team, you will apply your knowledge of deep learning to develop and evaluate models that have broad cross-organizational, global, and technology impact. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You will partner with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. You will be encouraged to see the big picture and be innovative to develop technologies that positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.

Key job responsibilities

• Research, design, implement, evaluate, and optimize machine learning solutions to push the boundaries in scene understanding technologies
• Work with internal and external stakeholders to help identify and translate business problems into scientific solutions
• Partner closely with engineering teams to prototype, deploy models to production, and drive scalable system designs
• Collaborate with team members and provide mentorship to more junior scientists and engineers
• Adhere to best practices in the team for code reviews, documentation, and knowledge sharing

BASIC QUALIFICATIONS

• Background in one or more of: machine learning, computer vision, audio processing

PREFERRED QUALIFICATIONS

• Ph.D. degree in engineering, computer science, machine learning, statistics, or related fields
• 2+ years of experience implementing computer vision or deep learning algorithms in Python, C++, or equivalent programming languages
• Demonstrated experience in conducting independent research to solve real-world customer problems
• Publications at top-tier peer-reviewed conferences or journals
• Excellent critical thinking skills, combined with the ability to present work clearly and compellingly in both verbal and written form
• Technical fluency; comfort understanding and discussing architectural concepts and algorithms, tradeoffs, and new opportunities with technical team members