Applied Scientist, Observability, Prime Video

Amazon Amazon · Big Tech · London, United Kingdom · Applied Science

Applied Scientist role focused on developing and deploying customized generative AI and large models for observability solutions within Prime Video. The role involves exploring emerging techniques for agentic solutions and machine learning algorithms for high-scale recommendations, with a focus on scaling models to large datasets and integrating research into production systems.

What you'd actually do

  1. Develop machine learning algorithms for high-scale recommendations problems
  2. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement
  3. Collaborate with software engineers to integrate successful experimental results into Prime Video wide processes
  4. Report and share results with the team and wider scientific community by authoring documents that are both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment

Skills

Required

  • PhD, or a Master's degree and experience in CS, CE, ML or related field research
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • Experience in building machine learning models for business application

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience in building machine learning models for business application

Other signals

  • develop and deploy customized models for PV builders needs at scale
  • explore emerging techniques that help us make better decisions faster for agentic solutions
  • develop machine learning algorithms for high-scale recommendations problems
  • lead the design of machine learning models that scale to very large quantities of data