Software Dev Engineer II ML, Amazon Business Marketing Science

Come be a part of a rapidly expanding $35 billion dollar global business. At Amazon Business a fast-growing startup passionate about building solutions we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential

Amazon Business ( is an online store that combines the selection convenience and value customers have come to know and love from Amazon with new features and unique benefits tailored to the needs of businesses. Amazon Business provides easy access to hundreds of millions of products everything from IT and lab equipment to education and food-service supplies. Amazon Business customers also enjoy a variety of benefits including business-only pricing and selection a multi-seller marketplace single- or multi-user business accounts approval workflow purchasing system integrations payment solutions tax exemptions dedicated customer support Business Prime and more. Our customers range from government entities with tens of thousands of users to sole proprietors.

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) to join the team to drive key science methods and delivery of the project working closely with science product and engineering leads as well as our leadership. Were a fast-growing team with high visibility from the leadership team and lots of new opportunities.

Key job responsibilities
-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 models 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.

About the team
The Marketing Science team applies scientific methods and research techniques to enhance our understanding of AB consumer behavior market trends and the effectiveness of marketing strategies. Our goal is to develop and advance theories and models that can be used to make informed decisions in marketing and to provide insights into consumer decision-making processes. Additionally we seek to identify and explore emerging trends and technologies in marketing and to develop innovative approaches for addressing the challenges and opportunities in the field.

- 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

- 3 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Bachelors degree in computer science or equivalent

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