Applied Scientist II (Level 5) - Search
Amazon Expansion and Exports (AEE) Search team creates, customer-focused Search ranking and relevance solutions. Our ranking models and services powers the experience when customer visits Amazon site worldwide and types in a query or browses through product categories. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day.
AEE Discovery team has a mission to solve customer problems that require advancing the state of the art in machine learning. We work backwards from the customer to create value for them by addressing an underlying, unsolved scientific problem. We deploy our solutions through distributed systems that operate at millisecond latencies at Amazon scale. We strive to publish our solutions and open-source our software so that the broader scientific community can benefit.
As an applied scientist on our team, your role is to leverage your strong background in Computer Science, Reinforcement Learning, and Machine Learning to help build the next generation of our model development and assessment pipeline, harness and explain rich data at Amazon scale, and provide automated insights to improve machine learned solution that impact millions of customers every day. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models and information retrieval system. We are particularly interested in experience applying natural language processing, deep learning, and reinforcement learning at scale. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.
Your responsibilities include:
· Analyze the data and metrics resulting from traffic into Amazon's product search service.
· Design, build, and deploy effective and innovative ML solutions to improve various components of the search stack, such as indexing, ranking, and query autocompletion.
· Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
· Publish and present your work at internal and external scientific venues in the fields of ML/RL/NLP/IR.
Your benefits include:
· Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers around the world.
· The opportunity to use (and innovate) state-of-the-art ML and RL methods to solve real-world problems.
· Excellent opportunities, and ample support, for career growth, development, and mentorship.
AEE Discovery team operates primarily out of Amazon's Austin office. We are a new and expanding team where you will have an opportunity to influence our goals and mission. We are a mix of applied scientists and software engineers who collaborate with other teams within Amazon Search to solve and deploy machine learning solutions at scale.
· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 3+ years of hands-on experience (academic or industrial) in predictive modeling and large data analysis.
· Strong coding and problem-solving skills in at least one programming language such as Python, Java, C++, etc.
· Working knowledge of web-scale data processing (e.g., Hadoop, Spark).
· Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.
· A PhD in CS, Machine Learning or in a highly quantitative field.
· Prior work experience as an applied scientist or a data scientist at a consumer product company.
· Experience using an object-oriented language to write production-ready code.
· Strong record of publications in one of the following areas: information retrieval, reinforcement learning, natural language processing, recommender systems, reinforcement learning, multi-armed bandits.
· Industry experience working with search engines, autocomplete, or recommender systems.