Applied Scientist II, Amazon Search

About the role

Amazon Search is reinventing how customers find products through natural-language and semantic understanding. We are looking for an Applied Scientist II to push the science behind Natural Language Search that interprets complex, constraint-rich shopping queries, retrieves and ranks the most relevant products.

Responsibilities

• Design, train, and ship deep-learning ranking and semantic-matching models that improve search relevance and reduce how often customers see irrelevant results, across hard query types.
• Build the training data and evaluation methods that make these models work: synthetic and historical labels, hard-negative mining, and targeted sampling at the cases where search fails.
• Develop signals that match product attributes to what the customer actually asked for.
• Run offline and online A/B experiments, analyze precision/recall tradeoffs, and iterate to launch.
• Work with engineers and scientists across teams to take models from prototype to production at Amazon scale.

Qualifications

• PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications

Preferred Qualifications

• Experience in machine learning, data mining, information retrieval, statistics or natural language processing
• 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
• Experience with A/B testing
• Experience in practical work applying ML to solve complex problems for large scale applications
• Publishations in ML, IR, or NLP venues (e.g., NeurIPS, ICML, SIGIR, KDD, ACL)
• Experience training large-scale or deep neural ranking/relevance models
• Experience taking ML models from prototype to production