Applied Scientist, AWS Polly

Amazon; Cambridge; On-site; GBP 55,000 - 85,000; Full time

Job summary

Amazon's AWS Polly seeks a passionate Applied Scientist with deep learning expertise to innovate in speech synthesis. This role encompasses advanced research, system design, and collaborative implementations across Amazon's extensive AI initiatives. Candidates should possess a strong educational background and practical experience in machine learning within production environments.

Qualifications

• PhD or Master's degree in CS/CE/ML or related field.
• Experience in patents or publications at top-tier conferences.
• Experience programming in Java, C++, or Python.

Responsibilities

• Research and develop advanced deep learning techniques for speech synthesis.
• Design and experiment with systems integrating text, phonetics, and audio.
• Collaborate with engineers and product managers to translate research into applications.

Skills

Deep Learning

Natural Language Processing

Machine Learning

Programming in Python

Algorithms and Data Structures

Education

PhD or Master's degree in Computer Science, Computer Engineering, or Machine Learning

Tools

Unix/Linux

Job description

AWS Polly is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background to help advance the state of the art in Generative AI for speech synthesis. Polly powers natural-sounding Text-to-Speech (TTS) voices at scale and is evolving into a platform that leverages Large Language Models (LLMs) and multimodal architectures to deliver expressive, high-fidelity speech across hundreds of languages and voices.

Key job responsibilities
As an Applied Scientist on AWS Polly team, you will be part of a fast-moving and collaborative environment where scientific innovation meets customer impact. You will research and develop advanced deep learning techniques, with a focus on LLMs and transformer-based architectures, to push the boundaries of natural speech generation. Your work will span areas such as prosody modeling, speech style transfer, multilingual synthesis, and controllable voice cloning, leveraging Amazon’s vast compute infrastructure and rich multimodal datasets.

You will be responsible for designing and experimenting with novel speech generation systems that combine text, phonetics, and audio to produce lifelike, contextually appropriate speech. Your models will be trained at scale and optimized for performance, latency, and quality to meet the rigorous standards of AWS production systems.

Collaboration is core to the role: you’ll work closely with other applied scientists, engineers, and product managers to translate research into customer-facing capabilities. You’ll also partner with teams across AWS AI/ML, including those working on Bedrock, Alexa, and Amazon Connect, to ensure Polly’s speech technology integrates seamlessly into broader generative AI experiences.

As a scientific leader, you will regularly communicate your findings through technical papers, internal design documents, and presentations. Your ability to distill complex models and experiments into actionable insights will influence the direction of both product development and future research initiatives at Amazon.

About the team
AWS Polly is Amazon’s cloud-based text-to-speech service. We deliver lifelike voices and scalable APIs to customers around the world. The Polly science team builds next-generation speech generation models using LLMs and multilingual training pipelines. We are part of AWS, driving innovation in multimodal generative AI.

This role is ideal for scientists who want to work at the intersection of speech, language, and deep generative modeling, and who are excited by the challenge of bringing research to production at scale.- PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in building machine learning models for business application- Experience using Unix/Linux
- Experience in professional software development

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