Amazon’s eCommerce Foundation (eCF) organization provides the core technologies that drive and power the Amazon website and the consumer experience. Millions of customer page views and orders per day are enabled by the systems eCF builds from the ground up. eCF Data enables business analytics and insights, providing data and data curation capabilities to thousands of internal and external customers worldwide.
Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading machine learning services. As a scientist in the BDT team, you'll partner with technology and business teams to build services that surprise and delight our customers. You will be working with petabytes of structured and unstructured data to help our customers derive critical insights and solve real-world problems. You'll design and implement cutting-edge distributed ML services from the ground up, design and run experiments, research new algorithms, and find new ways of optimizing the risk, profitability, and customer experience for a wide variety of business segments across Amazon. As part of this group, you will have the chance to work with a large team of thought leaders, engineers, and scientists in the distributed computing, machine learning, and business intelligence fields.
Applied science at Amazon is a fast growing field. This is a highly technical role that requires substantial cross-disciplinary interaction with software engineers, product managers, solution architects, business intelligence engineers, and other scientists. Besides theoretical analysis and innovation, you will work closely with software engineers to put your research, designs, and algorithms into practice. You will also work on cross-disciplinary efforts with other scientists and engineers at Amazon to establish scalable, efficient, automated processes for large-scale data analysis, ML model development, and model validation.
We’re looking for top scientists capable of using ML, computer science, distributed systems, and other techniques to design, implement, and evangelize state-of-the-art solutions for previously-unsolved problems.
· PhD and 2+ years of professional experience in computer science, statistics, engineering, mathematics, or related fields (or Master's Degree and 4+ years of professional experience).
· Specialization in machine learning, deep learning, data mining, computer vision, NLP, ASR, or related fields.
· Experience with object-oriented programming, datastructures and algorithms, and performant low-level coding.
· Experience with machine learning applications, frameworks, and libraries (e.g. TensorFlow, PyTorch, SciPy, SciKit-Learn, MXNet, etc.)
· Experience with Python, C/C++, or Java.
· PhD and 3+ years of professional experience with a proven track record of field-advancing thought leadership and contributions.
· Experience working effectively with software engineering teams.
· Experience rapidly prototyping new software components and services.
· Experience with High Performance Computing and Distributed Computing.
· Experience with Deep Learning and Reinforcement Learning.
· Experience with Reinforcement Learning applications, frameworks, and libraries (e.g. Keras-RL, RL Coach, Ray, RLLib, Tune, etc.)
· Excellent written and verbal communication skills, including a track record of writing and presenting insightful scientific reports on ML design in distributed systems and describing model performance using standard performance metrics.