The CreativeX RAPID(Real-time Ad Personalization & Insights Development) team is seeking passionate and talented MLE to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging cutting-edge technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods.
Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences.
Key job responsibilities
- Quickly acquire knowledge of cutting-edge technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain.
- Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems.
- Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models.
- Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications.
Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences.
Key job responsibilities
- Quickly acquire knowledge of cutting-edge technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain.
- Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems.
- Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models.
- Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications.