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PhD + ML industry experience, or Master's degree and 5+ years of Computer Science, Machine Learning or related field experience
- Experience in intellectual contributions to the field, evidenced by patents, publications in top-tier peer-reviewed conferences or journals, blog posts, or equivalent achievements.
- Experience and programming skills in at least one major programming language such as Java, C++ or Scala
Amazon is looking for a passionate, talented, and inventive applied scientist with Machine Learning and optionally Search background to join the TEN Search team. As an applied scientist, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission—real world incarnations of some of the most difficult academic problems.
Key job responsibilities
- Work on research projects in machine learning and related fields to create highly innovative customer experiences;
- Analyze large amounts of data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities;
- Validate models via statistically rigorous 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;
- Work side by side with fellow applied scientists in the team.
About the team
Our aim is to bring quality search results to customers around the world through world class machine learning and AI innovation.
Our team is based in Luxembourg, and includes scientists and machine learning engineers with education and experience from top institutions from across the globe.
Luxembourg, LUX
- Experience as a lead applied scientist in large-scale, data-driven environments, guiding complex projects from research to implementation
- Proven track record in addressing and solving complex, large-scale business challenges using machine learning, with demonstrable positive outcomes on business metrics.
- Strong experience in building consensus and driving solutions among multi-disciplinary teams across global locations, demonstrating effective multi-cultural communication.
- Knowledge of software engineering best practices, including coding standards, code reviews, source control management, build processes, and how to scale, test and make systems fault tolerant.
- Scientific publications track record
