Machine Learning Engineer - Content

Nielsen Media would not function without our Technology teams! We are catalysts for delivery quality, on-time, reliable measurements to clients, and we are cultivators, growing our employees through education, skill building and experiences. Around the globe, our Technology teams are relentless in our pursuit of superior analytics, technology, process and support.
The Machine Learning Engineer will work on new technology and research projectswithin the content recognition and classification area of Nielsen. You will be working toidentify and/or classify content from both audio and video. In order to make thesetechnologies, you should have a good theoretical background and practical experiencewith machine learning systems. Theoretical knowledge about audio or video is required,and you should be capable of performing signal processing on either audio or videodata. We expect you to be well versed in engineering best practices and softwaredesign concepts, and to be able to document and explain your work. You will coordinateyour work with the tasks of colleagues from your own team and other Nielsen teams.You will interact with Nielsen customers and partners as required while developing thenext content recognition or classification product. As time allows, you will advise andlead other engineering colleagues and student interns. You could also initiate orparticipate in the publication of scientific papers, and represent Nielsen at scientificconferences.

Requirements

• Bachelor’s degree in Computer Science and/or Electrical Engineering and at least 3 years of practical experience in multimedia signal processing and programming. Masters or PhD in Computer Science, Electrical Engineering, or a related field is preferred.
• Solid fundamentals (practical and theoretical) in machine learning and digital signal processing of either audio or video.
• Experience with accelerated machine learning tools such as Tensorflow, Keras,PyTorch, etc.
• Experience developing end to end project workflow in machine learning systems.
• This includes literature review, data collection, building ML system infrastructure, evaluation systems, and production/integration into a final service.
• Good programming skills in Python and C/C++. Good knowledge of debugging techniques and applications development.
• Knowledge of audio or video fingerprinting
• Experience handling large amounts of data and familiarity with databases. Bash and shell scripting experience.
• Very good knowledge of Linux/Unix platforms and Windows or Mac OS.
• Knowledge about visual perception, and/or audio and music theory.
• Experience with cloud services such as AWS or Google Cloud
• Quickly understand and properly judge the applicability of scientific publications
• and technical papers.