AWS Foundational Data Services (FDS) Santa Clara team is responsible for enabling customers to run their critical workloads in the cloud. The FDS Santa Clara team delivers high-performance applications and solutions for customers helping them modernize applications they run on AWS and achieve cost savings, security, scalability and resiliency . Team also provides customers ability to modernize their applications via refactoring, replatforming and rehosting techniques. We are seeking a ML Engineer to experiment with ML algorithms and tools, select appropriate datasets and data representation methods, perform feature engineering, model selection and validation, run machine learning tests and benchmarking, perform fine-tuning using test results, train and retrain systems and build prototypes. The ML focused SDE should understand deploying ML models to production, building components in a service, consider multiple design approaches, and make appropriate trade-offs for data and model parallelism at scale. The ML focused SDE should sufficiently be able to actively participate in technical and customer discussions within the team such as participating in code reviews, design discussions, operational reviews, and working backwards exercises with customers, peers and stakeholders.

Utility Computing (UC)
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for

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Key job responsibilities
* You demonstrate independence in ML model development applying a range of ML tools and algorithms.
* You demonstrate your ability to solve difficult problems that contain visible risks or roadblocks. You have solved problems without immediately obvious solutions, though the solution may appear obvious in hindsight.
* You have demonstrated your proficiency with the major lifecycle of software and ML model development including design, coding, model experimentation, tuning and model validation.
* You collaborate with data scientists and engineers in providing data engineering support and integrate with managed ML services. You are also capable of deploying ML models to an integration or production environment.
* You are active in review processes on your team (e.g., code reviews), providing meaningful feedback to others, including more senior engineers. You seek feedback on your own work actively and early enough to be actionable.
* You make improvements to your team’s development and experimentation processes.
* You communicate effectively to your team about the work you deliver.
* You mentor new teammates and/or interns to help them become productive contributors