Can Alexa help anyone experience the music they enjoy? Even if they don't know what they'd like to listen to in this moment? Or, if they know they want “Happy rock from the 90s”, can she help them find it?
Your machine learning skills can help make that a reality on the Amazon Music team. We are seeking a Senior Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval.
You'll work in a collaborative environment where you can pursue ambitious, long-term research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web.
The successful candidate will have a PhD in Computer Science with a strong focus on machine learning, or a related field, and 3+ years of practical experience applying ML to solve complex problems in recommender systems, information retrieval, signal processing, NLP or dialogue systems. Great if you have a passion for music, but this is not a requirement.Responsibilities:
- Advance long-term, exploratory research projects in machine learning and related fields to create highly innovative customer experiences;
- Analyze large amounts of Amazon’s customer data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities;
- Validate new or improved models via statistically relevant experiments across millions of customers;
- Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale;
- Technically lead and mentor scientists.