We are always looking for interns to work with us. Our interns publish their work in music, audio and deep learning conferences. We only consider applicants who are already in a PhD program and have a strong publication record. Drop me an email, if interested.

Contact: jordi < dot > pons < at > stability < dot > ai
Links:    LinkedIN
   Curriculum vitae (February 2020)    GitHub
   @jordiponsdotme    Google Scholar
  1. Stability AI   Since 2023 (Barcelona)
      Research on generative AI for music and audio.
  2. Dolby Laboratories   2019 – 2023 (Barcelona)
      Deep learning research on music and audio.
  3. PhD student  2015 – 2019 (Barcelona)
      UPF – Music Technology Group.
      Deep learning for music and audio tagging.
  4. Telefónica Research Internship      Summer 2018 (Barcelona)
      Training neural audio taggers with few data.
  5. Pandora Radio Internship  Summer 2017 (USA, Oakland – Bay Area)
      Deep learning for music tagging at scale.
  6. German Hearing Center  Summer 2015 (Hannover)
      Improving cochlear implant users music
    perception using source separation.
  7. Master in Sound & Music Computing
      2014 – 2015 (Barcelona)
      UPF – Music Technology Group.
  8. IRCAM Internship  Sept. 2013 – Aug. 2014 (Paris)
      Source separation for drums transcription.
  9. Telecommunications Engineer  2009 – 2014 (Barcelona)
      Universitat Politècnica de Catalunya.
  10. Conservatory  2002 – 2006 (Girona)
      Piano, clarinet and harmony studies.


  • Young Researchers in Computer Science Award, by the Sociedad Científica Informática de España (SCIE) – Fundación BBVA (2020).
  • Cum laude mention for my doctoral thesis on “Deep neural networks for music and audio tagging” (2019).
  • Finalist of the Big Data Talent Awards (2019) for my doctoral thesis on “Deep neural networks for music and audio tagging”.
  • Best student paper award for End-to-end learning for music audio tagging at scale in the 19th International Society for Music Information Retrieval Conference (ISMIR, 2018).
  • AI Grant Fellowship (2017) for the creation of Freesound Datasets.
  • Machine Learning award for the poster Towards a grounded deep learning paradigm for music modeling in the 5th DTIC-UPF Doctoral Student Workshop (2017).
  • Best paper award for Experimenting with Musically Motivated Convolutional Neural Networks in the 14th International Workshop on Content-Based Multimedia Indexing (CBMI, 2016).

Jordi Pons is a researcher at Stability AI working on generative models for audio and music. Previously, he was a staff researcher at Dolby Laboratories and received a PhD in music technology, large-scale audio collections, and deep learning at the Music Technology Group (Universitat Pompeu Fabra, Barcelona). He also recieved a MSc in sound and music computing (Universitat Pompeu Fabra, Barcelona), and his BSc was in telecommunications engineering (Universitat Politècnica de Catalunya, Barcelona). He also interned at IRCAM (Paris), at the German Hearing Center (Hannover), at Pandora Radio (USA, Bay Area), and at Telefónica Research (Barcelona).