- Palo Alto, CA
In this role you will be focused on improving the search results for hundreds of millions of Amazon.com customers around the world. Along with your teammates, you will constantly strive to improve search results and relevance ranking across all Amazon categories, as well as blending category results for "all product search". You will invent universally applicable signals and algorithms for training machine-learned ranking models and improve the machine-learning framework for training and offline evaluation that is used for all new relevance models.
• Build machine learning models for Product Search.
• Develop new ranking features and techniques building upon the latest results from the academic research community.
• Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.
• Focus on identifying and solving customer problems with simple and elegant solutions.
• Design, develop, and implement production level code that serves billions of search requests. Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment.
• Collaborate with other engineers and related teams within A9.com and Amazon.com to find technical solutions to complex design problems.
Joining this team, you'll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading e-commerce and Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.
• MS in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
• 5+ years of hands-on experience in predictive modeling and analysis
• Strong algorithm development experience
• Skills with Java, C++, or other OOP language, as well as with Python, R, MATLAB or similar scripting language
• Strong communication and data presentation skills
The ideal candidate will have a PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field, and 5+ years of relevant work experience, including:
• Significant peer reviewed scientific contributions in relevant field.
• Extensive experience applying theoretical models in an applied environment.
• Expertise on a broad set of ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian Non-Parametrics methods.
• Expert in more than one more major programming languages (C++, Java, or similar) and at least one scripting language (Perl, Python, or similar).
• Strong fundamentals in problem solving, algorithm design and complexity analysis.
• Strong personal interest in learning, researching, and creating new technologies with high commercial impact.
• Experience with defining organizational research and development practices in an industry setting.
• Proven track in leading, mentoring and growing teams of scientists (teams of five or more scientists).
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