Do you want the excitement of experimenting with cutting edge machine learning, natural language processing, computer vision, and active learning models to solve real world problems at scale? Imagine experimenting with Deep Neural Networks as your daily job and imagine using your model outputs to affect the product discovery of the biggest e-tailer in the world. Imagine leading research inside of an Amazon team that is always looking to deploy creative solutions to real world problems in product discovery. Your research findings are directly related to Amazon’s Browse experience and impact millions of customers, ingesting images, text and all the structured and unstructured attributes in the Amazon catalog to drive true understanding of products at scale.
The Amazon Browse Classification and Discovery team is seeking an Applied Scientist for developing ML systems that can help classify Amazon products into our catalog and build new experiences for improving customer discovery of products. You will be part of Browse AI team consisting of experienced Applied Scientist working on a new set of initiatives, building models and delivering them into the Amazon production ecosystem. Your efforts will build robust ensemble of ML systems that can drive classification of products with a high precision and recall, and scale to new marketplaces and languages. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).
We are looking for an experienced Applied Scientist who can develop best in class solutions. Your primary customers are Amazon shoppers who would thank you for correctly identifying products in our catalogs across countries and languages.
The ideal Applied Scientist candidate has deep expertise in one or several of the following fields: Web search, Applied/Theoretical Machine Learning, Deep Neural Networks, Classification Systems, Clustering, Label Propagation, Natural Language Processing, Computer Vision, Active learning, and Artificial Intelligence. S/he has a strong publication record at top relevant academic venues and experience in launching products/features in the industry.
· Master in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
· 3+ years of relevant experience in industry and/or academia.
· Fluency in at least one programming language (C++, Java, or similar) and one scripting language (Perl, Python, or similar).
· Familiarity with a broad set of supervised and unsupervised ML approaches and techniques ranging from Regression to Deep Neural Networks.
· Proven track record of successfully applying ML-based solutions to complex problems in business, science, or engineering.
· Hands on experience with Deep Learning
· PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
· Experience with fast prototyping.
· Experience working effectively with software engineering teams.
· Publications at top-tier peer-reviewed conferences or journals.
· 5+ years of relevant experience in industry and/or academia.
· Depth and breadth in state-of-the-art NLP and deep learning.
· Good written and spoken communication skills.
· Experience with modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-Reduce).