Description

The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences.

Key job responsibilities

• Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals

• Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality

• Research in advanced customer understanding and behavior modeling techniques

• Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification

• Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results

• Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems

• Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports

• Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team

About the team

The AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.

Basic Qualifications

• PhD, or Master's degree and 5+ years of applied research experience

• 5+ years of building machine learning models for business application experience

• Experience programming in Java, C++, Python or related language

• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with popular deep learning frameworks such as MxNet and Tensor Flow.

• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

• Proficient in 2 of these areas: large language models, information retrieval, recommender systems, knowledge graph, and graph neural networks.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.