Applied Scientist (GenAI/LLM), Sandstone

About the role

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create 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 fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge.

We are looking for scientists who are passionate about technology, innovation, and customer experience, and are 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. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!

Key job responsibilities

In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM 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.

Additional information

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Pay and benefits

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

USA, CA, San Diego - 142,800.00 - 193,200.00 USD annually

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually