DESCRIPTION
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 require specific mix of skills between devices 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 working with limited hardware resources.
Key job responsibilities
We are looking for an Applied Scientist with experience in building highly optimized Machine Learning models for speech generation.
As an Applied Scientist, you will:
- Work with the team on end-to-end development of an ML models 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 paves 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 then the ones hosted in cloud. We are cooperating directly with the teams developing devices and with scientists responsible for the cloud models in order to provide our customers best possible experience.
BASIC QUALIFICATIONS
- PhD in engineering, computer science, machine learning, mathematics or equivalent quantitative field
- Experience working in Speech Science
- Experience applying theoretical models in an applied environment
- Experience in state-of-the-art deep learning models 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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