Research Engineering Intern, Machine Learning for Embedded Audio (PhD)

November 17, 2020
1 Hacker Way, Menlo Park, California
Job Type


Facebook Reality Labs TED Audio has centralized audio functions such as transducers, acoustics, devices, and machine learning-based audio algorithms. The team is focused on algorithm development and commercialization of real-world products, with responsibilities to end-to-end audio experience. The current team consists of mostly Ph.D. engineers and scientists working on ML/ DSP based algorithms for AR/VR products. As an intern in our team, you will have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.

Our internships are twelve (12) to sixteen (16) weeks long and we have various start dates throughout the year.

Research Engineering Intern, Machine Learning for Embedded Audio (PhD) Responsibilities

  • Explore and research new and emerging machine learning based audio algorithm development for AR/VR products
  •  Work closely with ML/AI engineers and Audio algorithm scientists on productizing research algorithms

Minimum Qualifications

  • Currently has, or is in the process of obtaining a Ph.D. in Electrical Engineering, Computer Science, Computer Engineering, Applied Mathematics, or related field
  • Completed coursework on signal processing, image processing, or signal and systems
  • Hands-on experience with one or more deep learning/ML frameworks such as Tensorflow, PyTorch, or Keras
  • Strong programming skills in Python and/or C/C++
  • Interpersonal skills: cross-group and cross-culture collaboration
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications

  • Intent to return to degree-program after the completion of the internship/co-op

    Research publications in conferences or journals

    Experience with developing audio audio signal processing algorithms such as noise suppression, acoustic echo cancellation, sound field classifications or machine learning algorithms

    Knowledge of training data augmentation and large dataset preparations

    Experience with real-time systems

    Experience in working with GPU, cloud systems, profiling/low-level optimizations, Cuda/CuDNN

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