Applied Science Manager, GenAI Evaluation Media (GEM)

Amazon•Sunnyvale, CA

About The Position

Passionate about creating visual customer experiences that push the boundaries at the forefront of GenAI? The North America Stores GenAI Evaluation Media (GEM) team is seeking an experienced Applied Science Manager to help shape the future of visual shopping experiences. We're building CXs and foundational capabilities to understand, enhance, and generate real-time GenAI imagery, videos and CXs that inspire customers and drive purchase confidence, towards our vision to be the leader in visual media. Specifically, the charter will focus on visual agentic experiences, multi-modal personalization, vision language models, and real-time image/video generation, looking ahead as customer shopping continues to inspirational assistant-driven experiences. The Applied Science Manager will lead a team of Applied Scientists and Machine Learning Engineers, and drive the technical vision and execution of agentic AI capabilities that inform and guide the customer’s shopping journey through visuals. This includes developing core science primitives for multimodal understanding, visual content generation and editing, personalized virtual try-on, and automated quality assurance. Your team will build the foundational capabilities that enable customers to express and discover styles through multimodal conversation and receive personalized, visual responses that bring their ideas to life. The scientific approach emphasizes accurate, real-time visual understanding and generation, contextual understanding, and scalable personalization, enabling agentic AI to actively collaborate with customers to achieve their style goals. Your team will bring together computer vision, natural language processing, generative AI, and human-centered design to create agentic shopping experiences that are as intuitive as talking to a human specialist with a deep domain knowledge base. Success requires establishing robust metrics, working with multiple partners in cross-team collaboration, validating asset effectiveness across diverse customer touch points, and navigating rapid advances in AI technology to maintain competitive advantage. The ideal candidate will combine deep technical expertise in Computer Vision, Generative AI, or related fields with strong business acumen and strategic leadership skills. You will partner with stakeholders across Amazon to align priorities, drive innovation, and build a culture of scientific excellence and customer obsession. This role requires both strategic thinking and technical depth, with a focus on delivering practical solutions that scale. This is a unique opportunity to shape the future of visual commerce through applied AI research, leading a team that will define how hundreds of millions of customers discover and evaluate products and styles through visual experiences.

Requirements

3+ years of scientists or machine learning engineers management experience
PhD in computer science, machine learning, engineering, or related fields, or Master's degree
Experience hiring, developing, and managing high-performing technical teams
Master's degree in related field with 8+ years of industry experience
Experience building complex software systems, especially involving generative AI, machine learning and computer vision, that have been successfully delivered to customers
Experience building machine learning models or developing algorithms for business application
Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
Experience in e-commerce, retail or advertising
Experience understanding needs of business and end customers and translating them into right solutions
Experience working effectively across cross-functional teams and partnering well with people at all levels within an organization
Experience developing, deploying and managing AI products at scale

Responsibilities

Drive the technical vision and roadmap, aligning with business goals and customer needs
Lead development of core science primitives for vision and language understanding, visual content generation and editing, virtual try-on, and automated quality assurance via state-of-art computer vision, machine learning, generative AI
Design and implement visual agentic systems, balancing visual quality, relevance, latency, and cost
Establish metrics and success criteria for scientific initiatives, ensuring rigorous validation across customer touch points
Lead and develop a high-performing team of Applied Scientists and Machine Learning Engineers
Foster a culture of scientific excellence, innovation, and customer obsession
Recruit and retain top talent in computer vision, machine learning, and generative AI
Mentor and grow future technical leaders
Partner with multiple teams within Amazon
Facilitate learning and knowledge sharing within the team via presentations at internal and external science forums
Drive development and deployment of scalable agentic systems for visual content understanding and generation
Own end-to-end delivery of multiple concurrent research initiatives
Establish and maintain high scientific and engineering standards across the team
Stay current with latest advances in ML/AI and identify opportunities to apply them
Guide technical direction for complex problems while maintaining practical focus on customer value
Partner with product and engineering teams to deliver customer-facing features
Drive alignment across teams on technical approaches and resource allocation

Benefits

Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits .

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume