Ever wonder how you can keep the world’s largest selection also the world’s safest and legally compliant selection? Then come join a team with the charter to monitor and classify the billions of items in the Amazon catalog to ensure compliance with various legal regulations.
The Classification and Policy Platform team is looking for outstanding Applied Scientists to build technology to automatically monitor the billions of products on the Amazon platform. The software and processes built by this team are a critical component of building a catalog that our customers trust.
You will have an opportunity to work with cutting edge machine learning algorithms on large datasets. You will need to build Amazon scale applications running on Amazon Cloud that both leverage and create new technologies to process large volumes of data that derive patterns and conclusions from the data.
We are looking for highly motivated applied scientists and engineers interested in delivering the next level of innovation to product search for Amazon. As an Applied Scientist on the CPP team, you will be responsible for working across backend, client, business development, and data engineering teams to coordinate deep-dives, inform roadmaps, visualize metrics, and create predictive models to determine how we can best serve our customers.
· Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching and ranking problems, including filtering, new content indexing, and apply document understanding
· Conducting and coordinating process development leading to improved and streamlined processes for model development. Strong customer focus is essential
· Working closely with Product Managers to expand depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps
· Providing technical and scientific guidance to your team members
· Communicating effectively with senior management as well as with colleagues from science, engineering, and business backgrounds
· Being a cultural leader that ensures teams are collecting, understanding, and using data to inform every decision that impacts our customers
The successful candidate will have an established background in developing customer-facing experiences, a strong technical ability, a start-up mentality, excellent project management skills, and great communication skills.
· MS in Computer Science, strong knowledge of machine learning, and 5+ years of relevant experience in industry and/or academia OR PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
· 2 +/- years of experience using mainstream programming language (C++, Java, Python, or similar).
· 2 +/- years of experience using object oriented programming, data structures and algorithms, and software system design.
· 2 +/- years of experience using 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.
· PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
· 6+ years of relevant experience in industry and/or academia.
· Publications at top-tier peer-reviewed conferences or journals.
· Depth and breadth in state-of-the-art computer vision and machine learning technologies.
· Good written and spoken communication skills.
· Experience with modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-Reduce).
*Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.