Applied Scientist, Amazon Music Search
2 weeks ago
Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Bachelor's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field
Amazon Music Search team is seeking an 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 applied 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.
Key job responsibilities
- Use machine learning, deep learning, LLMs and NLP techniques to create scalable solutions for business problems
- Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes
- Design, development and evaluation of highly innovative models for predictive learning
- Work closely with software engineering teams to drive model implementations and new feature creations
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
- Research and implement novel machine learning and statistical approaches
About the team
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
- Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field
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