DESCRIPTION

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 Product Knowledge Classification team is seeking a Sr. 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 Product Knowledge Classification Science 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 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 Sr. 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.

Amazon Science gives you insight into the company's approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It's the company's ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.

Please visit https://www.amazon.science for more information.

BASIC QUALIFICATIONS

• MSc. in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
• 5+ years of relevant ML research 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.

PREFERRED QUALIFICATIONS

• PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
• 5+ years of relevant ML research experience in industry and/or academia.
• Experience with fast prototyping.
• Experience working effectively with software engineering teams.
• Publications at top-tier peer-reviewed conferences or journals.
• Depth and breadth in state-of-the-art NLP and deep learning techniques.
• Good written and spoken communication skills.
• Experience with modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-Reduce).