Applied Scientist II, Visual Search Science

About the role

The Visual Search Science team is pioneering generative AI for shopping within Amazon Search. We sit at the intersection of computer vision, natural language processing, and information retrieval building systems that help customers visualize what they're looking for and seamlessly discover matching products. Our team operates with speed and autonomy while leveraging Amazon's massive scale, GPU infrastructure, and product catalog. We are a tight-knit group of scientists and engineers who value rigorous experimentation, creative problem-solving, and shipping innovations that customers love. We collaborate closely with partner teams across Search organization.

Requirements

• 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 state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience using Unix/Linux
• Experience in professional software development
• Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
• Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS
• 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 disabi

Additional Information

Amazon Search is building a first-of-its-kind AI-powered visual search experience that lets customers describe products they're imagining, instantly see AI-generated images in response, and tap those images to search for matching products to shop. We are transforming the search engine into a shopping engine by leveraging advances in generative AI and multimodal understanding. We are seeking an Applied Scientist II to join the Visual Search Science team and push the boundaries of generative AI and multimodal retrieval at Amazon scale. You will work at the intersection of diffusion models, large language models (LLMs), and multimodal search to build systems that generate product visualizations in real time and connect them to Amazon's billions-scale catalog. The ideal candidate has deep expertise in one or more of the following areas: text-to-image generation, multimodal retrieval, LLM-based classification, AI safety and content moderation, or retrieval-conditioned generation. You will operate with startup-level autonomy backed by the resources of Amazon Search, serving hundreds of millions of customers worldwide through GPU-distributed inference systems that generate and rank results in real time. Key job responsibilities You will design, train, and optimize generative AI models for real-time product image generation, ensuring outputs meet strict latency requirements while maintaining high visual quality and query alignment. You will develop multimodal retrieval systems that connect AI-generated images to Amazon's billions-scale product catalog, optimizing for recall and ranking relevance across product categories. A core part of the role involves building LLM-based classifiers for visual intent detection, query understanding, and safety filtering within real-time latency budgets. You will advance AI safety science through defense-in-depth approaches including embedding-space classifiers, adversarial data engines, and post-generation content moderation. You will design and execute large-scale online experiments to measure impact on customer engagement, search success, and business metrics, defining evaluation frameworks that combine automated metrics with human judgment. You will collaborate with engineering, product, and design teams to architect GPU-intensive inference pipelines serving real-time traffic at scale, and contribute to Amazon's scientific community through publications and patents.

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