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Netflix

Research Engineer (L4) - Globalization

Posted 2 Days Ago
Be an Early Applicant
Remote
Hiring Remotely in USA
150K-750K Annually
Senior level
Remote
Hiring Remotely in USA
150K-750K Annually
Senior level
Research Engineer will conduct Machine Learning research to enhance localization and global entertainment, focusing on ASR, Speaker Diarization, and Audio Understanding.
The summary above was generated by AI

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

The Globalization Data Science and Engineering team is at the forefront of removing language barriers and providing a stellar member experience to all our members regardless of their language preferences. We are responsible for the translation and cultural adaptation of all aspects of member interaction, including beautiful localized user interfaces, subtitles, and dubbing of award-winning Netflix originals.

We are looking for an experienced Research Engineer with established track records in Automatic Speech Recognition (ASR), Speaker Diarization, and Audio Understanding technologies to develop and productize algorithms that power high quality localization at scale. In this rare opportunity, you will have the chance to conduct cutting-edge Machine Learning research to help enhance localization and global entertainment at Netflix. You will partner with a talented cross-functional team of scientists, engineers, product managers, and domain experts to deliver business impact.

Responsibilities

  • Research, develop and productize cutting-edge Automatic Speech Recognition (ASR), Speaker Diarization, and Audio Understanding models

  • Partner closely with other ML engineers and scientists to integrate work into business applications

  • Act as domain expert and thought leader in identifying new research opportunities with significant business impact

  • Present your research to all levels of the company clearly and concisely

About you

  • Proven track record of conducting cutting-edge research in Automatic Speech Recognition (ASR), Speaker Diarization, and Audio Understanding models, demonstrated by publications in top-tier conferences or journals

  • MS or PhD in ML, Computer Science or related fields

  • Familiarity with common ML and DL frameworks such as PyTorch or Tensorflow

  • Exceptional communication and collaboration skills

  • Comfortable with ambiguity; able to take ownership, and thrive with minimal oversight and process

  • Netflix culture resonates with you
    Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $150,000 - $750,000.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.

Top Skills

Audio Understanding
Automatic Speech Recognition
Machine Learning
PyTorch
Speaker Diarization
TensorFlow

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