Description Amazon is looking for talented Postdoctoral Scientists to join our Stores Foundational AI team for a one-year, full-time research position. The Stores Foundational AI team builds foundation models for multiple Amazon entities, such as ASIN, customer, seller and brand. These foundation models are used in downstream applications by various partner teams in Stores. Our team also invests in building foundational large language models, which can power conversational applications as well as machine learning tasks with scarce data (zero/few-shot learning). The postdoc is expected to develop machine learning techniques for Large Language Model (LLM) Alignment. Currently, Alignment techniques for LLMs rely primarily on the annotation of preference between alternative responses followed by policy (LLM) optimization either directly (DPO-style) or after learning one or multiple reward functions (PPO-style). However, the data from such annotation is often noisy and confounded by ambiguity, subjectivity, and multi-dimensionality of preference. The goal of the project is to develop 1) high-quality data annotation procedures with clear instructions, 2) preference models which account for noisiness, ambiguity, subjectivity, and multi-dimensionality of preference annotation, and 3) appropriate algorithms for directly optimizing the policy (LLM) or appropriate loss functions for learning rewards using such preference models. Key job responsibilities • Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. • Publish your innovation in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. Basic Qualifications • PhD in a relevant field, received within 2 years of starting the program • Proven publication record in Machine Learning, LLM, Optimization, Reinforcement Learning or other related technical fields * Experience in data science and quantitative research * Proficiency in technologies relevant to the subfield Preferred Qualifications • Ability to independently deliver results in a fast-paced environment • Publications at top-tier, peer-reviewed conferences and/or journals • Exceptional verbal and written communication skills • Expert knowledge in modeling and performance, operationalization, and scalability of scientific techniques and establishing decision strategies Required application materials: • CV, which lists all peer-reviewed publications and conferences, • Research statement that outlines your research achievements and future research interests, and • A journal article or book chapter that demonstrates your domain expertise. 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. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.