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Applied science manager (alexa international)

London
Amazon
Science
€100,000 a year
Posted: 10 June
Offer description

Requirements

* Experience directly managing scientists or machine learning engineers
* Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or PhD
* Knowledge of ML, NLP, Information Retrieval and Analytics
* Experience building complex highly-scalable systems that involve predictive models or applications of machine learning
* (Desirable) Experience building machine learning models or developing algorithms for business application
* (Desirable) Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers


What the job involves

* The Alexa International Science team is looking for a passionate, talented, and inventive Applied Science Manager with a strong background in speech models (understanding and generation) and deep learning, to lead a team building industry-leading Generative AI technology with speech-to-speech models and multilingual systems
* You will lead a team of Applied Scientists, drive cross-team scientific strategy for speech quality across international locales, influence partner teams, and deliver solutions with broad impact across Alexa's global products
* You will own programs with global visibility and interact with a cross-functional group of Science, Product, and Engineering leaders
* Your team's work will advance the state of the art in multilingual speech generation, text-to-speech synthesis, and speech-to-speech models, directly impacting customers across 20+ languages with natural, expressive, and locale-appropriate voice experiences for Alexa+
* You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in speech synthesis, voice quality, and pronunciation accuracy for non-English locales
* The ideal candidate possesses a solid understanding of machine learning, speech synthesis (TTS/S2S), multilingual phonetics, modern model architectures, and evaluation methodology
* They thrive in a fast-paced environment, tackle complex challenges in low-resource language settings, and are able to influence and align multiple teams around a shared scientific vision
* Lead and manage applied scientists focused on multilingual speech generation and voice personality
* Build and develop high-performing science teams focused on speech synthesis, pronunciation, evaluation, and multilingual model adaptation
* Set technical direction and raise the bar on scientific rigor, experimental methodology, and publication quality
* Hire, mentor, and grow scientists at multiple levels
* Cross-Organizational Collaboration
* Partner with cross org stakeholders to align goals and accelerate delivery
* Establish clear communication channels and workflows across organizational boundaries
* Drive alignment between science roadmaps and product launch timelines across 20+ locales
* Delivery and Execution
* Own end-to-end delivery of speech quality improvements from research through production deployment
* Define success metrics and evaluation frameworks for multilingual speech quality
* Balance long-term research investments with near-term launch commitments
* Lead and manage a team of Applied and Data scientists responsible for building and enhancing capabilities for Alexa+
* Collaborate with cross-functional teams to build methods to align Amazon’s LLMs with human preferences
* Identify and prioritize research opportunities that have the potential to significantly impact our AI systems
* Mentor and guide team members to achieve their career goals and objectives
* Communicate research findings and progress to senior leadership and stakeholders
* Rapidly experiment and drive productisation to deliver customer impact
* Drive academic partnership with top tier Indian university as part of the org’s AI/ML Center initiative
* Participate in and drive science publications in peer-reviewed venues of repute
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