Sponsored Ads is re-imagining the advertising landscape through advanced 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.
Amazon Ads is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day.
The principal applied scientist in this position will focus on designing, developing and launching innovative ad experiences delivered to advertisers and shoppers. Our team utilizes leading-edge science and engineering technologies in generative AI to help shoppers discover new products and brands through intuitive new interfaces. We also empower advertisers to setup efficient campaigns to reach shoppers. We collaborate with various teams across multiple Amazon organizations to push the boundary of what's possible in these fields.
• PhD in Machine Learning, Computer Science, Statistics or highly related field (Physics, Math)
• 7+ years of applied ML experience in building complex, real-time systems involving AI, ML, and NLP with successful delivery to customers
• Hands-on experience with designing and prototyping AI solutions for real world applications.
• Expert knowledge of machine learning algorithms and techniques
• Experience with large-scale data processing and storage systems
• Strong business acumen, proven track record in performing high-quality analysis of data at scale
• Ability to communicate complex technical concepts and solutions to all levels of the organization• Experience with Flow Matching and Diffusion Models in the context of multi-modal architectures
• Experience building large-scale machine-learning models and infra for online recommendation, ads ranking, personalization, or search
• Experience with natural language processing and generative model architectures
• Demonstrated track record of innovative AI solution development
• Research publications or patents in generative AI technologies
• Track record of mentoring scientists
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https://amazon.jobs/content/en/how-we-hire/accommodations pour plus d'informations. Si le pays ou la région dans lequel vous postulez ne figure pas dans la liste, veuillez communiquer avec votre partenaire de recrutement.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit
https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
