Machine Learning Engineer
- East Bay, CA, United States
- San Francisco, CA, United States
- Silicon Valley, CA, United States
We are looking for someone interested in applying the latest advances in ML to building cutting-edge entertainment experiences for our 40MM monthly users.
Here are some example projects:
Create a recommendation system that provides personalized recommendations to users by leveraging the data generated by hundreds-of-millions of stories being read. The system must be fast, scalable and support testing different candidate recommendation algorithms.
For our top stories we work with actors to create audio versions of our story. For the rest of the catalog we have experimented with text-to-speech systems but have found the results more comical than convincing. Build open state-of-the-art TTS systems (wavenet etc) to create TTS system that takes into account semantic context. What emotion needs to be conveyed? Can we condition on this to guide the TTS system?
Hooked readers generate vast amounts of granular data when they read -- every tap is a data point. Can we use marry this behavioral information with automatic story analysis using NLP techniques to predict engagement on future stories?
Hooked is using machine learning to disrupt Hollywood. We’re redefining entertainment for teens and millennial with 40M monthly users. We’ve raised funding top investors in Silicon Valley and Hollywood, including Greylock, SV Angel, Cowboy Ventures, William Morris Endeavor, Sound Ventures (Ashton Kutcher), Greg Silverman (ex-President of Warner Bros), Charles King, Snoop Dogg, Jamie Foxx, Mariah Carey, Stephen Curry, Andre Iguodala, Kevin Durant and others. We’re building the media company of the future - the next Netflix, the next Disney.
We're a warm, friendly, positive group of people. The company has a family-vibe. We love what we do and have complete trust and faith in our team. We are intrinsically motivated to crack the code on mobile storytelling and all the challenges that come with such an ambitious goal. We are highly efficient and prioritize work-life balance. We love the excitement of startup life — of building quickly, testing and iterating, and continuously improving. We love the close connection we have with our millions of users. There's nothing better than the thrill of a consumer entertainment startup.
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