With our global footprint, it is imperative that we provide a hyper-personal experience for our customers. One aspect of personalization is to let customers watch our content in their preferred language. The Localization Core Services team is seeking a passionate and talented machine learning expert with a strong background in digital signal processing, and machine learning technologies. In this role, you will investigate, conceptualize, design, implement, and validate new algorithms that will help get our content localized in shorter time and with better quality.
Some examples of the problems you might tackle in your new role
End-to-end multi languages speech synthesis using neural networks
Build applications for Emotion Detection, Lip Sync Detection, Voice Correction, Speaker detection/diarization.
Cloud Dubbing – Building tools to enable recording and mixing of dubs in the cloud.
Enhancing quality of Speech to Text engines and their relevance to generate dubbing scripts
Utilizing Computer vision algorithms to manage lip sync of dubbed audio to text.
Using NLP and Text processing on subtitles to detect emotion, sentiments, negative words etc




PhD degree in Computer Science, Applied Statistics, or related field Or published research papers in the field of Machine learning and Speech.
Strong background in machine learning using supervised and unsupervised methods. Bonus points for expertise in deep learning and factorization machines.
Experience with Python and/or Java and good coding practices.
Experience with TensorFlow, Torch and/or Keras.
Intuitive understanding of machine learning algorithms, optimization methods and statistical tools.
Proven track record of leveraging large amounts of data to solve real-world problems
An aptitude for learning fast – new tools, new algorithms, new programming languages, new models.
Desire and willingness to continue growing your capabilities as machine learning engineer.

Educational level:

Ph. D.

How to apply:

Please mention NLP People as a source when applying


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About Netflix

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