Machine Learning Engineer, Sponsored Products Off-Search Sourcing and Relevance

Amazon.com, Inc.
New York, United States of America
4 days ago

Role details

Contract type
Internship / Graduate position
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

New York, United States of America

Tech stack

Artificial Intelligence
Code Review
Computer Programming
Data Mining
Software Design Patterns
Distributed Systems
Information Retrieval
Machine Learning
Natural Language Processing
Software Engineering
Information Technology
Low Latency
Build Process
Machine Learning Operations
Software Coding
Software Version Control
Data Pipelines

Job description

Design, build, and operate ML infrastructure and data processing pipelines that power ad relevance and sourcing at massive scale

  • Develop and optimize machine learning models incorporating deep product and shopper understanding to identify relevant advertisements across non-Search surfaces
  • Architect and build ad serving systems that solve real-world customer use cases with high volume, low latency, and strict availability requirements
  • Integrate ML solutions with large-scale distributed systems for click-through prediction and ad auction
  • Measure impact and customer response through rapid A/B experimentation, iterating to improve relevance and performance
  • Collaborate with Product Managers and Scientists to translate complex business problems into scalable ML-driven solutions

About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

Requirements

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
  • Bachelor's degree in computer science or equivalent
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing

About the company

Build the machine learning models and infrastructure that deliver relevant, personalized ad experiences across non-Search surfaces on Amazon.com - from product detail pages to the homepage - incorporating deep product and shopper understanding to identify the most useful advertisements for hundreds of millions of customers. You'll drive ML innovation at Amazon Ads scale with direct, measurable customer impact. The Off-Search Sourcing and Relevance team within Sponsored Products develops state-of-the-art ML models, large-scale data pipelines, and low-latency ad serving systems that power discovery beyond Search. We conduct rapid A/B experimentation to ensure we surface the most relevant ads to downstream systems for click-through prediction and auction. You'll work with Product Managers and Scientists to solve complex problems at scale - building ML infrastructure, driving product initiatives, and launching solutions in one of Amazon's fastest growing and most profitable businesses.

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