Interested in Machine Learning, and empowering the world to do more and better machine Learning? Amazon SageMaker, Amazon Web Service's (AWS) Machine Learning platform team is building customer-facing services to catalyze data scientists and software engineers in their machine learning endeavors. This product is a blend of HTTP API's, low and high-level SDK's, and an AWS Console UI.
The SageMaker Environments team is a new team in SageMaker. We are building a service that lets data scientists and developers easily control the exact environment in which their code runs. With SageMaker Environments, customers write and execute code in a predictable way without having to build containers. For example, customers can deploy a Jupyter notebook to SageMaker training or hosting without ever touching Docker. This seamless code motion works across a broad spectrum of services including SageMaker Hosting and Batch Transform, AWS Batch, ECS, EKS, Lambda, Glue and ECR. Using SageMaker Environments together with AWS Step Functions, customers can build complex workflows working with just the code they wish to run
You will design, implement, test, document, and support cross-cutting services to help customers do machine learning at scale. You'll assist in gathering and analyzing business and functional requirements, and translate requirements into technical specifications for robust, scalable, supportable solutions that work well within the overall system architecture. You will serve as a key technical resource in the full development cycle, from conception to delivery and maintenance. You will produce comprehensive, usable software documentation; recommend changes in development, maintenance and system standards.
You will own delivery of entire piece of the system and serve as technical lead on complex projects using best practice engineering standards, and hire/mentor junior development engineers.
We're moving fast, and this is a great team to come to to have a huge impact on AWS and the world's customers we serve!
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