The ML team at Bose CE Applied Research is looking for a co-op to work on machine learning and deep learning for audio understanding as well as MIR for six months starting July 2021. 

The applicant must be eligible to work in the US as an intern/co-op. 

Contact: Please send your resume to Shuo Zhang ( 

Machine Learning Co-op Spring 2021

At Bose, we’ve spent more than 50 years finding new ways to bring pioneering audio products to millions of people in their homes, cars, planes, and just about anywhere else people enjoy their music. To succeed for the next 50 years, we must continue to drive innovation that delivers on our human-centered brand promise to help people be more, feel more, and do more. 

The Machine Learning team in CED Applied Research at Bose is looking for a passionate Machine Learning Co-op to work at the intersection of Artificial Intelligence and User Experience. The duration of this position is 6 months starting July 2021 (full-time 40 hours/week).

What you can expect:


  • Work as part of a passionate and collaborative team of ML, iOS, embedded, DSP, and cloud engineers
  • Your vision will influence the direction of future Bose Consumer Electronics products
  • Gain hands-on experience in applied machine learning and deep learning for audio, DSP, software engineering, model deployment, etc.


What you’ll do:


  • Work on a focused six-month ML research project in audio signal processing with a mentor
  • Work on a variety of audio understanding problems with machine learning and deep learning
  • Prune and compress deep learning models for deployment on edge devices or embedded targets
  • Research, implement, and evaluate a variety of published approaches and algorithms for problems in ML and audio signal processing


What we’re looking for:


  • Strong programming background with 2+ years of experience in Python and/or Java
  • Strong knowledge and experience working with audio and DSP preferred
  • Undergraduate or graduate computer science, electrical engineering or related major with experience building ML systems for audio signal processing and/or music information retrieval applications
  • Familiar with latest research in deep learning for audio and able to implement algorithms from research papers
  • Strong experience in implementing deep neural networks with Tensorflow, Keras, PyTorch, etc.
  • (Desired) Experience and interest in deploying models to edge devices
  • (Desired) Experience and interest in working on audio perception problems such as speech enhancement, voice pick-up, SED/SELD, MIR, etc.
  • (Desired) Publications in communities such as ISMIR, DCASE, ICASSP, etc.


 Bose is an Equal Opportunity Employer that is committed to inclusion and diversity. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status or any other legally protected characteristics.

It’s the end of the year again and I am due to write a post about my conference and community involvement this year. 

This year I have attended a number of conferences online due to COVID. This is not as good as attending in person and connecting with colleagues and friends. These include: 

  • ISMIR 2020
  • ICASSP 2020
  • DCASE 2020
  • ICML 2020
  • NLP4MusA Workshop @ ISMIR 2020

Invited conference peer reviews I have done this year in NLP and audio signal processing:

  • ACL 2020
  • EMNLP 2020
  • DCASE 2020
  • ISMIR 2020
  • NLP4MusA Workshop @ ISMIR 2020
  • EUSIPCO 2020
  • ECNLP workshop @ WWW 2020

As usual, most conferences have low acceptance rate, which is reflected in my portion of the reviews this year. Despite that, I did learn a lot about a variety of topics, and I have grown my ability to evaluate a paper properly by doing thorough background research when needed. 

Talks given this year (virtual):

  • Artificial Intelligence Festival APAC
  • NLP4MusA Workshop @ ISMIR 2020
  • Artificial Intelligence Festival by AI Accelerator Institute

Even though I wasn’t able to attend these events in person, it’s great this year I got to connect with many people in the community virtually and we discussed collaboration possibilities and I helped people with their data science and machine learning problems, including people at organizations such as the, the AIAI community, Northwestern University, Pandora, Ohio State University, University of Helsinki, Liveperson, NYU, Georgetown, U. of Edinburgh, UPF, etc. I enjoy these interactions as I also learned so much from each of them.

It’s about time to wrap up the cycle this year. The review work for next year has already started. Looking forward!