Your browser cookies must be enabled in order to apply for this job. Please contact support@jobscore.com if you need further instruction on how to do that.

Audio Research Engineer

Software Engineering - Applied Research | Emeryville, CA | Full Time

Job Description

Are you ready to revolutionize entertainment?

Gracenote is an entertainment data and technology provider powering the world’s top music services, automakers, cable and satellite operators, and consumer electronics companies. At its core, Gracenote helps people find, discover and connect with the entertainment they love. Daily, Gracenote processes 35 billion rows of data and is quickly becoming a world-leader in return path “big data.” Over the past 3 years, the company has grown to more than 2000 employees in 17 countries, including over 600 of the world’s top engineers with a passion for music, video, sports, and entertainment technology. Founded in 1998, Gracenote is one of America’s most iconic and respected media companies.  

Gracenote's Applied Research team is presently looking for an Audio Research Engineer.

Gracenote is an entertainment data and technology provider powering the world’s top music services, automakers, cable and satellite operators, and consumer electronics companies. At its core, Gracenote helps people find, discover and connect with the entertainment they love. Daily, Gracenote processes 35 billion pieces of data and is quickly becoming a world-leader in return path “big data.” Over the past 3 years, the company has grown to more than 2000 employees in 17 countries, including over 600 of the world’s top engineers with a passion for music, video, sports, and entertainment technology. Founded in 1998, Gracenote is one of America’s most iconic and respected media companies.  

We are presently looking for an Audio Research Engineer to join the Applied Research team at Gracenote. This team develops cutting edge technologies relating to music and audio, including media recognition, machine listening, data processing pipelines, and recommendation systems. In your role on the team, you will help develop and disseminate these technologies throughout the company and to customers by developing algorithms and tools, creating demo applications, and writing production system components.

Applicants should include a cover letter.

FOR THIS ROLE WE ARE LOOKING FOR INDIVIDUALS THAT HAVE:

  • Good programming skills in Python, C/C++, and Matlab
  • Solid fundamentals (practical and theoretical) in machine learning and digital signal processing
  • Interested in working on an ever-changing list of audio related projects
  • Enthusiastic about audio, music, music data, and solving problems in this space
  • Self starter capable of working independently and across a variety of engineering teams
  • Masters or PhD in Computer Science, EE, or a related field preferred

DESIRABLE:

  • 2+ years of professional experience in machine learning with audio
  • Cross platform experience - Linux, Windows, OS X
  • Versatile candidates with experience in a variety of other languages such as Swift, Objective C, Java, JavaScript, Scala, etc.
  • Familiarity with music and music technologies (e.g. MIDI, music theory)
  • Bash and shell scripting experience
  • Experience handling large amounts of data and familiarity with databases
  • Experience with accelerated machine learning tools such as Tensorflow, Keras, Theano, Spark etc.
  • Experience developing end to end project workflow in Machine learning systems - literature review, data collection, building ML system infrastructure, evaluation systems, and productionizing / integration into services
  • Experience building native applications for Mac, Windows, iOS, and Android
  • Experience with cloud services such as AWS or Google Cloud
  • Experience setting up cloud systems involving the Apache technology stack

Gracenote, a Nielsen company, is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, protected veteran status or any other protected class.