About the job
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.
Responsibilities
Solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon.
Drive end-to-end GenAI & Machine Learning projects that have a high degree of ambiguity, scale, complexity.
Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions.
Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them
Research new and innovative machine learning approaches.
Qualifications
Minimum
3+ years of building models for business application experience
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Experience applying theoretical models in an applied environment
Preferred
Experience building machine learning models or developing algorithms for business application
Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
Experience implementing algorithms using both toolkits and self-developed code