Sr. Applied Scientist
- Herndon, VA
Alexa Video group is responsible for voice enabling several video experiences on popular devices like Fire TV, Echo Show, Roku, Xbox etc. We are building a voice first video search and recommendation system which can help users find engaging video content from a vast catalog of videos from Prime Video, Netflix, Hulu, Disney+, ESPN, CNBC etc.
In this role, you will build cutting-edge large-scale machine learning models to improve quality of video content served to Alexa customers be it the home screen or search results page on several Alexa powered devices. These models will apply state of the art online learning techniques and will be trained on a variety of speech, text, visual, customer implicit and explicit preferences and video content features and will improve both recall and precision of our video search and recommendation results. These models will scale to multiple languages and countries and will provide inference within a few milliseconds' latency. You will also lead a team of scientists and collaborate with engineers to build, train and deploy these models. You will focus on unearthing customer problems and coming up with novel ways to solve these. You will propose hypotheses, validate these offline and run A/B tests to validate them online. As part of these activities, you will develop models that serves millions of video requests.
You will work with larger Amazon science community and contribute towards advancing the state of art on voice search via research papers, publications and patents.
Joining this team, you'll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN).
• PhD degree with 4 years of applied research experience or a Masters degree and 6+ years of experience of applied research experience
• 3+ years of experience of building machine learning models for business application
• Experience programming in Java, C++, Python or related language
• Significant peer reviewed scientific contributions in Natural Language Processing, Speech Recognition, Information Retrieval, Computer Vision or relevant field.
• Expertise on a broad set of ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian Non-Parametrics methods
• Extensive knowledge and practical experience in several of the following areas: search, recommendation systems, personalization, machine learning, deep learning, Natural Language Processing and dialogue systems.
• Proven track in leading, mentoring and growing teams of scientists (teams of five or more scientist)
• Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
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