Sr. Manager, Applied Science, Alexa Wakeword
Alexa is the groundbreaking voice service that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We're working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience and help create a cutting edge product used every day by people you know.
As a Sr. Manager of Applied Science in Alexa Wakeword, you will lead a team of scientists and engineers to drive key initiatives for developing and advancing world-leading wakeword solutions for any voice-driven Alexa product. You will contribute to the multi-year business and research roadmap and strategy for Alexa Wakeword. You will inspire your team to push the envelope and continuously produce innovative ideas, conduct insightful scientific experiments to explore these ideas and lead the integration of the proven ideas to products. You will influence design and architecture of software stacks offline and online for building and deploying flexible and efficient algorithm artifacts to production. You will review scientific work cross-functionally, promote scientific best practices and contribute to scholarly publications to help raise the bar of scientific research inside and outside Amazon.
• Graduate degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or related fields.
• Excellent written and spoken communication skills.
• Strong interpersonal skills
• Proven track records of managing science teams, hiring and developing top talents.
• Strong technical curiosity and the ability to invent.
• Proficient with programming languages such as C/C++ and Python.
• Proficient in scientific best practices and statistical analysis
• Deep domain expertise in machine learning including modern deep learning techniques
Familiarity with some of the following areas is a plus
• Privacy sensitive machine learning including federated learning
• Reinforcement learning
• Semi-supervised learning techniques for audio, acoustic and speech applications
• Unsupervised learning techniques for audio, acoustic and speech applications
• Quantization aware training
• Transfer learning
• Active learning
• Domain adversarial training
• PhD with a specialization in machine learning.
• Familiarity with automatic speech recognition is a plus
• Strong publication records.
• Strong software design and development skills.
• Experience working effectively with science, data processing, and software engineering teams.
• Proven track records of innovations and advancing the state of the art.
• Entrepreneurial spirit combined with strong problem solving skills.