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3+ years of non-internship professional software development experience
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2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
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Experience programming with at least one software programming language
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Experience building complex software systems that have been successfully delivered to customers
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(Desirable) 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
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(Desirable) Bachelor's degree in computer science or equivalent
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(Desirable) Experience in machine learning, data mining, information retrieval, statistics or natural language processing
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(Desirable) Experiences with LLM, prompt engineering
What the job involves
We are looking for a Machine Learning Engineer II who can drive appropriate technology choices for the business, lead the way for continuous innovation, and shape the future of ad serving on Amazon search
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You will build services to handle billions of requests per day, while maintaining response latency in milliseconds and meeting strict SLA requirements
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It is quite routine for our systems to operate on massive datasets using distributed frameworks
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You will design and code, troubleshoot, and support high volume and low latency distributed systems
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The solutions you create would ensure relevant ads are served to Amazon's customers
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You will directly impact our customers’ shopping experience while helping our sellers get the maximum ROI from advertising on Amazon
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This role will provide exposure to innovations in product search, information retrieval, natural language processing (NLP), LLM training, deep learning, and image processing
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In addition to being a strongly motivated IC, you will also be responsible for mentoring junior engineers and guiding them to deliver high impacting products and services for Amazon customers and sellers
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Drive the direction of our technical solutions, and work on many different technologies such as deep learning, AWS, Auto ML, real-time ML serving systems
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Design, develop, and production software to support scalable offline machine-learning pipelines and online serving components
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Work closely with applied scientists to optimize the performance of machine-learning models, improve the team’s machine learning productivity, and advance the technical foundation to empower our science innovation. What you create is also what you own
