Sr. Applied Scientist, Amazon Music - Catalog

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Sr. Applied Scientist at Amazon Music focused on developing and scaling AI/ML solutions for music understanding, curation, and engagement. The role involves shaping scientific direction, identifying GenAI and multimodal AI opportunities, and collaborating with engineering and product teams to launch scalable AI solutions that improve music discovery and personalization. Key responsibilities include inventing innovative AI/ML solutions, driving rigorous experimentation, and providing architectural guidance.

What you'd actually do

  1. Help shape the scientific direction of the organization by proposing state of the art modeling approaches, driving experimentation, and balancing scientific rigor with execution speed to deliver measurable customer impact.
  2. Think strategically about the future of GenAI and multimodal AI, identifying opportunities to transform music understanding, curation, and engagement.
  3. Stay at the forefront of advancements in GenAI, recommendation systems, and large-scale machine learning, driving adoption of new techniques where they create meaningful customer value.
  4. Collaborate closely with engineers across Music Intelligence, Personalization, Search and other partner teams to support long-term product and CX goals.
  5. Mentor applied scientists and engineers while actively contributing to the broader science and ML community across Amazon Music.

Skills

Required

  • PhD, or Master's degree and 6+ years of applied research experience
  • 5+ years of building machine learning models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Domain expertise in either Recommender Systems, Search or Ranking

Nice to have

  • Experience with Catalog Metadata, behavioral segmentation at scale
  • Experience with real-time ML systems (online scoring, streaming data, anomaly detection)
  • Experience working with large-scale customer data platforms or data lake architectures
  • Strong publication track record in top AI/ML Conferences (e.g. Recsys, KDD, ICLR, ICML, NeurIPS, etc.)

What the JD emphasized

  • building machine learning models for business application experience
  • Domain expertise in either Recommender Systems, Search or Ranking
  • Strong publication track record in top AI/ML Conferences (e.g. Recsys, KDD, ICLR, ICML, NeurIPS, etc.)

Other signals

  • GenAI
  • multimodal AI
  • recommendation systems
  • large-scale machine learning
  • music intelligence
  • personalization
  • content understanding
  • customer engagement