Amazon Sr Applied Scientist GenAI CreativeX Amazon Advertising 

United States, California, Sunnyvale 

455496782

As a Sr. Generative AI 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 opportunity
Key job responsibilities
This role is focused on generating image/text and building the related foundational models for generative AI. 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 and technical program 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 and deploy 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.

- 6+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with GenAI model training and tuning

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- 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.