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Machine Learning Engineer, Amazon
2 months ago
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products.
Actions, Insights, and Recommendation Solutions (AIRS) is at the forefront of our amazing growth machine enabling our teams to deliver at scale. Our goal is to scale account management multifold by investing in strategic self-service applications that improve productivity of external advertising customers and internal account management executives. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
As part of our team evolution we are investing in improving our understanding of the advertisers on Amazon through advanced ML modeling and building an ML service that delivers recommendations to advertisers and solves the prioritization and selection of most optimum recommendations and measure impact with explainability. Explainability of campaign recommendations delivered to advertisers is the #1 ask from advertisers and Account teams to improve adoption rates.
We are moving fast and have the ability to shape our tech infrastructure that will combine science and scalable engineering at a rapid pace. We are looking for a Machine Learning Engineer (MLE/SDE) to join the team to drive key science methods and delivery of the project, working closely with product and engineering leads, as well as our leadership. We’re a fast-growing team with high visibility from the leadership team and lots of new opportunities.
Key job responsibilities
As an Machine Learning Engineer on this team, you will
-Lead end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
-Focus on Model Deployment, including all tasks necessary to turn a prototype into a production model, such as 1) model data pipelines: building data pipelines to produce inputs for training and inference in both online and offline contexts. 2) Training and inference pipelines: orchestration of model training and inference jobs. 3) Post-inference work: work required after the model’s output to serve business needs such as integration with engineering systems.
-Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.
-Contribute to our ML Infrastructure, such as enhancements to our live model and experimentation platform.
-Maintenance and Operational Excellence: Regular maintenance and monitoring expected of any complex system/service.
-Contribute to building an infrastructure that facilitates end-to-end ML workflows.
- 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
- Experienced in large scale AI and ML infrastructure, AWS ML tools such as SageMaker
