Do you want to join the team working on the bleeding edge technology?
Have you ever wondered how we can give voice to the devices?
Or join the team developing GenAI models?
Our team is working on all of the above, join us to see yourself.
Text-to-Speech on Device team is responsible for development AI based voice models working locally on the devices.
This requires a specific mix of skills between device integration, voice generation technologies and machine learning.
We are delivering solutions for multiple customers, including offline solutions for Alexa, automotive customers and accessibility voices for visually impaired users.
All our models are integrated for devices and work with limited hardware resources.
Key job responsibilities
Work with the team on end-to-end development of an ML model for speech generation, from early experimentation to building production-ready models
Engage in state-of-the-art and innovative research in areas such as Speech Generation, Gen-AI, model compression, and knowledge distillation
Invent optimization techniques to push the boundaries of deep learning model training and inference
Create and propose detailed theoretical specifications for novel research ideas and directions, and rigorously justify their correctness
Train custom Speech Generation and Gen-AI models that beat the state-of-the-art and pave the path for developing production models
Collaborate with other science teams to bring state-of-the-art Speech Generation models from cloud to devices
About the team
Text-to-Speech on Device team is focused on delivery of low-footprint AI models for speech generation that can work locally on devices (Android, FireOS, etc.).
These models require much less computation power than the ones hosted in cloud.
We cooperate directly with the teams developing devices and with scientists responsible for the cloud models in order to provide our customers the best possible experience.
Basic Qualifications
PhD in engineering, computer science, machine learning, mathematics or an equivalent quantitative field
Experience working in Speech Science
Experience applying theoretical models in an applied environment
Experience in state-of-the-art deep learning model architecture design and deep learning training and optimization and model pruning
Experience implementing algorithms using toolkits and self-developed code
Experience with programming languages such as Python, Java, C++
Preferred Qualifications
Experience with model optimization techniques (quantization, distillation, compression, inference optimization, etc.)
Experience in professional software development
Experience communicating across technical and non-technical audiences, including executive-level stakeholders or clients
Experience working with agile development methodologies
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