Learn languages by playing a game. It. Backed by Kleiner Perkins, NEA and USV.
About the role
We are looking for an experienced AI Research Engineer to join our Speech team. The ideal candidate will have a proven track record as an AI research engineer, with experience across various machine learning techniques, and a particular focus on speech modeling: ASR, TTS, speech-to-speech models, audio tokenization, and more. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Proven experience as an AI research engineer, for example, in speech recognition, TTS, LLMs, or related fields
- Strong background in training and fine-tuning large models in an applied setting
- Advanced degree in Computer Science, Engineering, or a related field with a focus on machine learning or artificial intelligence, or equivalent experience
- Excellent leadership and communication skills, with the ability to manage and inspire a team
- Deep understanding of machine learning concepts, frameworks, and best practices
More about this role
Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in!
At Duolingo, you’ll join a team that cares about finding innovative solutions to complex technical problems , running countless experiments (300+ at a time!) with our massive user base to make data-driven decisions, and educating our users and employees alike. You’ll have limitless learning opportunities, mentorship and collaboration with world-class minds, and a variety of projects with large scopes — while doing work that’s both fun and meaningful.
Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world.
We are looking for an experienced AI Research Engineer to join our Speech team. The ideal candidate will have a proven track record as an AI research engineer, with experience across various machine learning techniques, and a particular focus on speech modeling: ASR, TTS, speech-to-speech models, audio tokenization, and more. They will have experience at all levels of the ML stack, including feature engineering, developing training data, fine-tuning, reinforcement...
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