Amazon Applied Scientist GenAI CreativeX Amazon Advertising 

United States, Washington, Seattle 

627543191

As an Applied Scientist on this team, you will:
* Drive end-to-end GenAI projects that have a high degree of ambiguity, scale and complexity.
* Build Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
* Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
* Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.
* Identify and action data collection and labelling in conjunction with team members.
* Research new and innovative machine learning approaches.
* Present results and explain methods to senior leadership.Why you will love this opportunityKey job responsibilities
This role is focused on computer vision, latent diffusion models, and the related foundational models to produce generative imagery and videos. You will develop core models that will be the foundational of the core advertising-facing tools that we are launching. You will conduct literature reviews to stay on the cutting edge of the field. You will regularly engage with product managers, who will partner with you to productize your work.A day in the life
On a day-to-day basis, you will be doing your independent research and work to develop models, you will participate in sprint planning, collaborative sessions with your peers, and demo new models and share results with peers, other partner teams and leadership.

- 3+ years of building ML models
- PhD, or Master's degree and 5+ years of CS, CE, ML or related field experience
- Experience programming in Python or C++ or a related language
- Experience with GenAI model training and tuning

- * Published relevant research work in leading ML conferences or journals.
- * Experience in building and deploying large-scale machine-learning models for computer vision, natural language processing, ranking, or personalization, etc.
- * Experience working with large real-world data sets and building scalable models from big data.