Amazon Business is one of Amazon’s fastest growing new initiatives focused on building solutions to enable business customers to research, discover and buy business, industrial and scientific products in large catalogs; across multiple devices, marketplaces and regions. Our customers include individual professionals, businesses and institutions that buy in either high frequency or in bulk quantities. Our customers have different and frequently more complex needs than the traditional Amazon customer base.
As an SDE on the team, you will develop design patterns, APIs, and highly scalable services that make the B2B use cases intelligent. You will build and leverage customer profile, user and organization attributes and their relationships, to offer personalized and differentiated experiences to AB customers. You will have to work across the AB domain and diverse set of teams and enable them to leverage customer profiles. You will have an opportunity to learn from seasoned engineers and learn/practice cutting edge technologies. We are looking for passionate engineers who are hungry for impactful work and willingness to experiment and learn.
· BS in Computer Science, or equivalent background in data structures, algorithms, object-oriented design and systems architecture. · 0-2+ years professional experience building and operating scalable distributed systems across the full software lifecycle including design, implementation, testing, operations, and maintenance. · Fluency in one or more modern programming languages such as Java, C# or C++. · Experience across front-end user interfaces, business logic, and data tiers. · Experience serving as technical lead, including mentorship of more junior software developers.
Good problem solving skills
Good understanding of CS
some experience or strong interest in the following topics:
designing internet-scale public APIs.
building solutions for enterprises, context-awareness, pervasive computing, and/or application of machine learning
working with modern tools for big data storage and analysis (e.g., AWS, Apache Spark, Hadoop, SQL, NoSQL
Foundational machine learning models and concepts: regression, random forest, boosting, GBM, NNs, HMMs, CRFs, MRFs, deep learning.