Music Information Retrieval Specialist

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At Epidemic Sound we are reinventing the music industry. Our carefully curated catalog, with over 32,000 tracks, is tailored for storytellers, streaming services, and in-store soundtracks. Countless clients around the world, from broadcasters, production companies, DSPs, and YouTubers rely on our tracks to help them tell their stories. Epidemic Sound’s music is heard in hundreds of millions of online videos daily, across millions of playlist streams, and in thousands of in-store locations. Headquartered in Stockholm, we’re spread across offices in New York City, Los Angeles, Seoul, Hamburg, Amsterdam, and Sydney. We’re growing fast, have lots of fun, and are taking the music industry with us.

The growth of our business requires us to be excellent at building and maintaining relationships with our customers to inspire action and loyalty. To achieve this, we need to make our user experience next level, through intuitive machine learning and customer analytics.

We are now looking for an experienced research scientist in music information retrieval (MIR) with a strong focus on machine learning (ML).
 

Job Description

The position as a Music Information Retrieval Specialist will report to the Head of Machine Learning and work in a hybrid team with central and embedded machine learning engineers delivering machine learning to various departments throughout the company. The use cases range from classifying time-frequency domain representations and forecasting symbolic representations (including lyrics) – to building fantastic music recommenders to further personalize Epidemic Sound’s offering.

You will be working closely with backend machine learning engineers and data engineers in deploying fair, explainable and interpretable models. You will improve the personalization of the music browser by:

  • Keeping up with (and contributing to) the latest state-of-the-art in music informatics and deep learning
  • Developing classifiers to identify type and feel of musical audio
  • Helping out with recommender systems (e.g. cold start) so that the music our users see first, is relevant to them based on their behaviour
  • Contributing to the automation of previously manual tasks, by leveraging the classification systems you have contributed to building
  • Consulting on appropriate implementation of algorithms in practice – and actively identifying new use cases that can help improve Epidemic Sound!


What are we looking for?

We are looking for a team member with a “no task is too small” mindset. It would be music to our ears if you have:

  • PhD or MSc in MIR
  • Extensive understanding of machine learning - including deep learning
  • Solid grasp of musical terms like timbre, chroma, tempo, key signature, etc.
  • Knowledge of MIR/audio analysis terms like acoustic fingerprinting, phase vocoder, short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCCs)
  • Experience with: TensorFlow, Keras, PyTorch, scikit-learn, SciPy, NumPy, Pandas or similar
  • Experience with machine learning in production
  • Fluency in Python programming and a passion for production ready code
  • Experience with Google Cloud and Docker

Curious about our music? Find our music on Spotify here → https://open.spotify.com/user/...

We have lots of fun soundtracking the world and our annual Spring Bash is an event that captures this perfectly. Take a look at our most recent celebration!


Application

Do you want to be a part of our fantastic team? Please apply by clicking the link below!

We believe that bringing people together from different backgrounds, experiences and perspectives makes for a healthy workplace, a more successful business and a better world. We value diversity and encourage everyone to come and soundtrack the world with us.

Or, know someone who would be a perfect fit? Let them know!

Department

HQ (Stockholm)

Västgötagatan 2
118 27 Stockholm Directions info@epidemicsound.com View page

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@epidemicsound.com
  • Charlotte Jansson
  • Chili
  • Edit
  • Gizmo
  • Grus
  • Hanna Seger
  • Henrik Preifors
  • Jean-Pierre Shammass
  • Lakrits
  • Linnea Hagströmer
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