Sesame builds personal agents for curious people. Follow a thought. Work out an idea. Discover something new. Preview available now on iOS. Coming to intelligent eyewear in 2027. Backed by Sequoia and Redpoint.
About the role
New and novel approaches are needed to realize all of our product goals. As a Machine Learning Scientist at Sesame, you are a research-oriented person with experience in NLP, Speech, and/or Computer Vision with a focus on deep learning. You are up to date on recent papers and approaches and you have the creativity and the intuition to come up with new solutions based on the application at hand. Contribute to the development of our ML models across multiple modalities.
What they're looking for
- Experience working independently in high-ambiguity environments
- Published papers in NLP, Speech, or Computer Vision involving large-scale deep learning
- Familiar with the state of the art in AI
- Bachelor’s degree or higher in CS or related field
- Masters/PhD desired
- Experience working with products
More about this role
Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive.
New and novel approaches are needed to realize all of our product goals. As a Machine Learning Scientist at Sesame, you are a research-oriented person with experience in NLP, Speech, and/or Computer Vision with a focus on deep learning. You are up to date on recent papers and approaches and you have the creativity and the intuition to come up with new solutions based on the application at hand.
Contribute to the development of our ML models across multiple modalities.
Work across the ML stack, including model architectures, data curation, model evaluation, training & inference infrastructure, research, and experimentation.
Pick promising approaches from the literature to bet on, and create new...
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