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Summary

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 real world products, with responsibilities to end to end audio experience. 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.

Required Skills

  • 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 Qualification

  • Currently has, or is in the process of obtaining a PhD 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 Qualification

  • 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
Job Overview

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