Applied Scientist, GenAI Evaluation Media

Amazon•Sunnyvale, CA

17d

About The Position

We are seeking an Applied Scientist to join our Generate AI Visual Media - Core Experiences Science team. This organization drives significant business value by revolutionizing how customers interact with product imagery and videos across Amazon. Working at the intersection of Computer Vision and Generative AI, you will help customers make confident purchase decisions through optimized visual content. Our goal is to transform shopping into a personalized journey of inspiration, discovery, and evaluation by helping customers evaluate how products will fit and flatter their unique self before they ship. In this role, you will be responsible for building scalable computer vision and machine learning (CVML) models, and automating their application and expansion to power customer-facing features. We are seeking domain expertise specifically in 3D Generative AI and Inverse Rendering. Relevant topics include Neural Fields, Implicit 3D Representations, NeRFs, Differentiable Rendering/Simulation, and Physically Based Rendering (PBR) Materials. We expect strong expertise at the intersection of Computer Graphics, Computer Vision, and Deep Learning. An inclination towards Generative AI, such as GANs, VQ-VAE/GAN, and Diffusion Models, is essential. Additionally, experience in application-specific domains such as Neural Avatars, Parametric Body Models, and Differentiable Rendering/Simulation will be highly advantageous.

Requirements

PhD, or Master's degree and 5+ years of CS, CE, ML or related field experience
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice To Haves

Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience with popular deep learning frameworks such as Tensor Flow and PyTorch.
Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

Responsibilities

Explore, collect, and process data
Frame and transform ambiguous business challenges into science hypotheses. Design and implement offline and online experiments to evaluate them
Develop prototypes to test new concepts/proposals for models and algorithms
Design and build automated, scalable pipelines to train and deploy ML models