Sr Applied Scientist, Applied AI Solutions

Amazon•Seattle, WA

About The Position

As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use. We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day—putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in. The Role We're seeking a talented Senior Applied Scientist with expertise in large language models, agentic systems, and foundational models. You'll accelerate our customer-facing features, contribute to our collaborative and innovative culture, and bring state-of-art applied research that raises the bar for the entire team.

Requirements

3+ 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 neural deep learning methods and machine learning
Experience using managed ML/AI solutions
Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

Nice To Haves

Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production
Build, optimize, and deploy ML models while collaborating with software engineers for productionization
Research innovative machine learning approaches and identify new opportunities for GenAI applications
Perform hands-on analysis and modeling of large datasets to develop actionable insights
Establish scalable, automated processes for data analysis, model development, and validation
Present results to senior leadership and collaborate with cross-functional teams