Applied Scientist (GenAI/LLM), Sandstone

About the Role

Join an innovative team of scientists and engineers who leverage terabytes of data and state-of-the-art Generative AI algorithms to push the boundaries of AI creativity. We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs, and Large Model training techniques that fuse general world knowledge, customer shopping behavior, and Amazon e-commerce domain knowledge. We are looking for scientists passionate about technology, innovation, and customer experience, ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers.

Key Job Responsibilities

In this role, you will leverage your background and expertise to lead developing foundational behavioral models for Amazon Stores using Generative AI, LLMs, and Large Model training techniques. On a day-to-day basis, you will:

• Research and implement new algorithms and architectures for generative AI applications.
• Optimize model performance and scalability for inference and deployment.
• Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation.
• Experiment with SOTA methods to improve generative AI model quality.
• Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Basic Qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• 2+ years of building models for business application 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
• Experience in patents or publications at top-tier peer-reviewed conferences or journals

Preferred Qualifications

• Strong understanding of machine learning, deep learning, and generative AI principles and algorithms.
• Proficiency with modeling and large-scale data processing tools such as Pytorch, scikit-learn, Spark MLLib, PySpark, MxNet, Tensorflow, numpy, scipy etc.
• Ability to work with large datasets and knowledge of data preprocessing techniques such as Hadoop, Spark etc.
• Familiarity with natural language processing (NLP) and computer vision for generative AI applications.
• Strong problem-solving and critical thinking skills and excellent communication and teamwork abilities to collaborate with cross-functional teams.
• Ability to stay updated with the latest advancements in generative AI and adapt to new techniques and methodologies.