Applied Scientist, Amazon Music - Search Science

Amazon Amazon · Big Tech · Sunnyvale, CA · Machine Learning Science

Applied Scientist at Amazon Music focused on using ML, deep learning, LLMs, and Agentic AI to create scalable solutions for music classification, recommender systems, dialogue systems, NLP, and music information retrieval. The role involves analyzing large datasets, designing, developing, and evaluating AI models, and working with engineering teams to implement and deploy these models at scale, with a focus on improving customer experiences.

What you'd actually do

  1. Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems
  2. Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes
  3. Design, development and evaluation of AI models for predictive learning
  4. Work closely with software engineering teams to drive model implementations and new feature creations
  5. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
  6. Research and implement novel machine learning and statistical approaches

Skills

Required

  • building models for business application
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience programming in Java, C++, Python or related language
  • building machine learning models or developing algorithms for business application
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • Unix/Linux
  • professional software development
  • patents or publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • scalable solutions
  • large amounts of Amazon's data
  • model development
  • model validation
  • model implementation
  • large scale data analyses
  • statistically relevant experiments across millions of customers

Other signals

  • ML models
  • LLMs
  • Agentic AI
  • large scale data analyses
  • model development
  • model validation
  • model implementation
  • algorithmic ideas at scale
  • statistically relevant experiments across millions of customers