Machine Learning lifecycle automation and MLOps concepts gained massive traction in recent years because of the rapid increase in applying ML to solve customer facing problems inside and outside Amazon.
We are looking for SDEs at all levels to join our team and help accelerate ML lifecycle automation and MLOps best practices adoption across all of CDO.
Are you looking for an opportunity to learn new cutting-edge skills and have an Amazon-wide impact? This cross-functional team will focus on building state of the art ML automation features critical to CDO use-cases in close partnership with the AWS SageMaker team.
As an engineer on this team you will be working closely with Principal Engineers in CDO and SageMaker to define common ML lifecycle best practices, and to design and build pioneering world-class ML automation features on top of SageMaker for multiple domains and use-cases.
This is a unique opportunity to gain technical expertise in highly sought-after skills at Amazon scale, to learn SageMaker technologies, and to apply these skills to create production pipelines for the entire ML model lifecycle from training to inference.
Your contributions will have a major impact by enabling, not only CDO orgs, but also external AWS customers who operate at Amazon scale.
Our ideal candidate is collaborative, innovative, and interested in working on the intersection of Machine Learning, Software Engineering, and AWS ML architecture.
We include sample prioritized features that you will be working on below to demonstrate the scale and the impact of this opportunity.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
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