Research Scientist, Machine Learning for Speech Enhancement
- Woodinville, WA
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.
Facebook Reality Labs brings together a world-class team of researchers, developers, and engineers to create the future of virtual and augmented reality, which together will become as universal and essential as smartphones and personal computers are today. And just as personal computers have done over the past 45 years, AR and VR will ultimately change everything about how we work, play, and connect.We are developing all the technologies needed to enable breakthrough AR glasses and VR headsets, including optics and displays, computer vision, audio, graphics, brain-computer interface, haptic interaction, eye/hand/face/body tracking, perception science, and true telepresence. Some of those will advance much faster than others, but they all need to happen to enable AR and VR that are so compelling that they become an integral part of our lives.The audio team at Facebook Reality Labs is looking for experts in machine learning in speech and audio. This role is focused in design, development and engineering of advanced machine learning for speech enhancement and noise suppression that drive audio experiences in AR and VR. An ideal candidate will be passionate about development of advanced proof-of-concept demonstration platforms on one hand and about pushing the state-of-the-art by conducting fundamental research on the other hand.
- Independently implement state-of-the-art models and techniques on Pytorch, Tensorflow or other platforms.
- Independently identify, motivate, and execute on reasonable medium to large hypotheses (each with many tasks) for model improvements through data analysis, and domain knowledge, and are able to communicate your learnings effectively.
- Design, perform, and analyze online and offline experiments independently with specific and well thought-out hypotheses in mind.
- Generate reliable, correct training data with great attention to detail.
- Identify and debug common issues in training machine learning models such as overfitting/underfitting, leakage, offline/online inconsistency independently and consistently.
- Work with research scientists to minimize footprint of trained ML models with various pruning, compression and real-time adaptation techniques.
- Effectively implement trained models on different platforms, including popular portable personal computing systems with AI accelerators.
- Understand the model architecture used, and the consequences of this for different hypotheses tested.
- Independently resolve most online and offline issues which affect the hypothesis testing.
- Be aware of common systems considerations and modeling issues, and factor this into modeling choices.
- PhD in a relevant field such as Deep Learning, Machine Learning, Audio signal processing, Speech signal processing, Computer Science, Statistics or equivalent work experience.
- 3+ years experience with development and implementation of speech and audio processing or deep learning algorithms.
- 3+ years experience with scientific programming languages such as Python and C++.
- 3+ years experience with deep learning frameworks, such as Pytorch or Tensorflow.
- Understanding of computer science fundamentals such as data structures and algorithms.
- Experience with applied statistics.
- Experience implementing and evaluating working and end-to-end prototypical learning systems.
- Experience with active noise cancellation, speech synthesis, audio-visual learning.
- Experience with building models on speech or acoustic datasets.
- Proven track record of achieving significant results and innovation as demonstrated by first-authored publications and patents.
- Experience working in cross-group and cross-culture teams.
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