Sr. Applied Scientist, Ads
Listed on 2025-12-13
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IT/Tech
Machine Learning/ ML Engineer, AI Engineer, Data Scientist, Data Analyst
Description
Amazon Ads is re‑imagining advertising through cutting‑edge generative artificial intelligence (AI) technologies. We combine human creativity with AI to transform every aspect of the advertising life cycle—from ad creation and optimization to performance analysis and customer insights. Our solutions help advertisers grow their brands while enabling millions of customers to discover and purchase products through delightful experiences. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground in product and technical innovations.
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.
Applications will be accepted on an ongoing basis.
Why You’ll Love This RoleThis role offers unprecedented breadth in ML applications and access to extensive computational resources and rich datasets that will enable you to build truly innovative solutions. You'll work on projects that span the full advertising life cycle, from sophisticated ranking algorithms and real-time bidding systems to creative optimization and measurement solutions. You'll work alongside talented engineers, scientists, and product leaders in a culture that encourages innovation, experimentation, and bias for action, and you’ll directly influence business strategy through your scientific expertise.
What makes this role unique is the combination of scientific rigor with real‑world impact. You’ll re‑imagine advertising through the lens of advanced ML while solving problems that balance the needs of advertisers, customers, and Amazon’s business objectives.
Amazon Ads is investing heavily in AI and ML capabilities, creating opportunities for scientists to innovate and make their marks. Your work will directly impact millions. Whether you see yourself growing as an individual contributor or moving into people management, there are clear paths for career progression. This role combines scientific leadership, organizational ability, technical strength, and business understanding. You'll have opportunities to lead technical initiatives, mentor other scientists, and collaborate with senior leadership to shape the future of advertising technology.
Most importantly, you’ll be part of a community that values scientific excellence and encourages you to push the boundaries of what’s possible with AI.
Watch two Applied Scientists at Amazon Ads talk about their work:
Learn More About Amazon AdsKey job responsibilities- Research and implement cutting‑edge ML approaches, including applications of generative AI and large language models
- Develop and deploy innovative ML solutions spanning multiple disciplines – from ranking and personalization to natural language processing, computer vision, recommender systems, and large language models
- Drive end‑to‑end projects that tackle ambiguous problems at massive scale, often working with petabytes of data
- Build and optimize models that balance multiple stakeholder needs - helping customers discover relevant products while enabling advertisers to achieve their goals efficiently
- Build ML models, perform proof‑of‑concept, experiment, optimize, and deploy your models into production, working closely with cross‑functional teams including engineers, product managers, and other scientists
- Design and run A/B experiments to validate hypotheses, gather insights from large‑scale data analysis, and measure business impact
- Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience
- 5+ years of building machine learning models for business application experience
- PhD, or Master’s degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed…
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