Applied Scientist II

Job ID: 1321977 | Services LLC


The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, our Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day.

Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Strategic Relevance team builds solutions to improve search quality and effectiveness for Grocery and Media categories (Video, Music, Books, Aps etc). In this role you will:
· 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 across Amazon 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 (AMZN), one of the world's leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.


· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language


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 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).