Machine Learning Engineer, Search Relevance
Amazon.com
The Amazon Search Relevance team owns ranking relevant results when customers search for products on Amazon. Our systems and algorithms operate on one of the world's largest product catalogs, surfacing the right products - with a high relevance bar and strict latency constraints. We are looking for Machine Learning Engineer to join our Search MLOps team to help accelerate ML lifecycle automation and MLOps best practices adoption across all of CDO.
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 Senior Software 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.
Key job responsibilities
1. Design and build scalable infrastructure that enables training, evaluating and deploying machine learning models over billions of collected historical data points
2. Design and develop tools for monitoring the performance of machine learning models at scale
3. Design and develop lineage and artifact tracking infrastructure for training data, ML models and experiments
4. Design and develop reproducible ML Pipelines orchestrating various components for ML models.
5. Embrace and champion engineering best practices within your group and beyond
6. Produce clean, high-quality code, tests, and written documentation
We are open to hiring candidates to work out of one of the following locations:
Palo Alto, CA, USA
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 Senior Software 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.
Key job responsibilities
1. Design and build scalable infrastructure that enables training, evaluating and deploying machine learning models over billions of collected historical data points
2. Design and develop tools for monitoring the performance of machine learning models at scale
3. Design and develop lineage and artifact tracking infrastructure for training data, ML models and experiments
4. Design and develop reproducible ML Pipelines orchestrating various components for ML models.
5. Embrace and champion engineering best practices within your group and beyond
6. Produce clean, high-quality code, tests, and written documentation
We are open to hiring candidates to work out of one of the following locations:
Palo Alto, CA, USA
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