Machine Learning Engineer, Sponsored Products and Brands

Sponsored Products and Brands (SPB) helps millions of advertisers reach shoppers across Amazon's stores worldwide. Behind every ad we show are machine learning models that predict click-through rate (CTR) and conversion rate (CVR). These predictions decide which ads are relevant, how they rank, and how advertisers get value. Our team builds and runs the ML infrastructure behind those models, end to end: - Offline feature engineering: a multi-tenant platform used by several ML teams. It runs thousands of daily feature jobs over petabyte-scale data and produces the features that train models in every marketplace we serve. - Model training: the standard system that retrains CTR and CVR models every day. How fresh these models are directly affects advertiser results and Amazon's revenue. - Online feature store and feature retrieval: low-latency, high-throughput services that provide features when a model makes a prediction. These predictions feed the scores used in real-time ad ranking. - Next-generation research platform: a platform that uses AI agents to help scientists explore, test and launch new model ideas faster. As an SDE II, you'll design, build and run distributed systems that work at very large scale, both in offline batch processing and online serving. You'll work closely with applied scientists and engineers to make models fresher, help scientists try ideas faster, and keep the systems that support ads ranking reliable. Key job responsibilities - Design and develop ML infrastructure for advanced recommendation models, from training through real-time inference, that delivers highly relevant ads to shoppers worldwide - Improve the scalability, automation and efficiency of large-scale training and real-time inference systems - Work closely with applied scientists to optimize the performance of ML models and infrastructure, and take models from experimentation into production - Deliver ML solutions end to end: design, implementation, testing, deployment and operations - Collaborate with engineers and scientists across the team to build systems that serve ads across all Amazon platforms and surfaces - Participate in design and code reviews and mentor peers A day in the life You might start the day checking the health of the overnight feature and training pipelines. Then you'd work with scientists on how to support a new model architecture, and spend the afternoon building a performance improvement for the online feature store. You'll ship often, see your work affect ads served around the world, and influence how ML infrastructure grows across the organization. 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. We're the team behind the full ML lifecycle for Sponsored Products recommendation models, from raw data to features, training and real-time serving. We value ownership, getting things done, and close collaboration with science partners. We invest in mentorship and in tools that make engineers and scientists more productive, including AI-assisted development. Minimum qualifications - 3+ years of software development engineer or related occupational experience - 3+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience - 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience - 1+ years of machine learning, statistical modeling, data mining, and analytics techniques experience - Experience programming with at least one software programming language Preferred qualifications - 1+ years of mathematics optimization such as linear programming and nonlinear optimization experience - Master's degree in Computer Science, Engineering, Mathematics, or a related field 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. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, NY, New York - 158,100.00 - 213,800.00 USD annually USA, WA, SEATTLE - 143,700.00 - 194,400.00 USD annually