• Design and train large-scale transformer and hybrid foundation models • Own model architecture choices across text, multimodal, and emerging paradigms • Diagnose and resolve training instabilities at scale • Navigate scaling tradeoffs across data, compute, and architecture • Define the technical direction for next-generation models • Apply strong fundamentals in deep learning and representation learning • Design and modify transformer architectures, including: Attention variants, RoPE, ALiBi, Grouped Query Attention (GQA), Mixture-of-Experts (MoE) • Build models from first principles, not just adapt pre-existing codebases • Own optimizer and scheduler choices, including: AdamW, Lion, Adafactor • Understand and debug optimizer instability, gradient pathologies, divergence at large scale • Validate scaling laws and navigate compute vs. data tradeoffs

• Conduct exploratory factor analyses (EFA) using Principal Axis Factoring, oblimin rotation, and parallel analysis • Develop and compare confirmatory factor analysis (CFA) models for independent and dependent variable constructs • Perform multiple linear regression analyses examining three dependent variable subscales across seven independent variable clusters using both unadjusted and covariate-adjusted models • Evaluate model assumptions and interpret statistical findings • Produce publication-quality statistical tables and figures • Prepare a publication-ready manuscript for submission to a peer-reviewed medical education journal • Draft the Introduction, Methods, Results, and Discussion sections • Present statistical findings in a clear and scientifically rigorous manner • Incorporate feedback from project investigators throughout the manuscript development process • Develop clean, well-commented, reproducible R code • Maintain organized analytic documentation and workflows • Collaborate with the research team through regular virtual project meetings

• Research real estate agencies across Australia using Google, agency websites, LinkedIn, online directories, and other publicly available sources. This may also include contacting agencies to obtain or verify publicly available contact information. • Identify and source accurate contact details for key decision-makers, including business owners, principals, directors, office managers, department heads, and other relevant contacts. • Accurately enter and maintain agency and contact information in HubSpot/CRM, ensuring records are complete, consistent, and free of duplicate entries. • Record key agency information, including agency name, contact person's name, job title, email address, phone number (where available), website, and location. • Ensure all contact information is sourced from legitimate, publicly available sources and follows the provided research and CRM processes. • Performance Expectations: Consistently source and accurately enter approximately 15 to 30 high-quality agency records per hour, while prioritizing accuracy and completeness over speed. The initial goal is to build a database of approximately 10,000 real estate agency contacts. • Quality Standards: Ensure all contacts are relevant to real estate agencies and sourced from legitimate, publicly available sources. Do not use purchased or low-quality email lists, guessed or fake email addresses, or create duplicate records. Follow the provided research process and maintain accurate, up-to-date CRM records at all times.

Role Description Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. - Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. - Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. - Identify new opportunities for research in order to meet business goals. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. - Telecommuting may be permitted. Qualifications - Master’s degree or foreign equivalent in Computer Science, Machine Learning, Engineering, or a related field and two years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. - Employer will accept a Bachelor’s degree or foreign equivalent in Computer Science, Machine Learning, Engineering, or a related field and five years of research or work experience in the job offered or a related occupation as equivalent to the Master’s degree and two years of research or work experience. - Must have one year of research or work experience in the following skill(s): programming in Java, C++, Python, or equivalent programming language; and conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering. - Telecommuting may be permitted. Requirements - The base pay range for this position in New York City is USD $183800 - $248700 (yr). - Pay is based on market location and may vary depending on job-related knowledge, skills, and experience. - A sign-on payment and restricted stock units may be provided as part of the compensation package, in addition to a full range of medical, financial, or other benefits, dependent on the position offered. - Applicants should apply via Amazon's internal or external careers site. Preferred Qualifications - Master’s degree or foreign equivalent in Computer Science, Machine Learning, Engineering, or a related field and two years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. - Employer will accept a Bachelor’s degree or foreign equivalent in Computer Science, Machine Learning, Engineering, or a related field and five years of research or work experience in the job offered or a related occupation as equivalent to the Master’s degree and two years of research or work experience. - Must have one year of research or work experience in the following skill(s): programming in Java, C++, Python, or equivalent programming language; and conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering. - Telecommuting may be permitted. Benefits - A sign-on payment and restricted stock units may be provided as part of the compensation package. - A full range of medical, financial, or other benefits, dependent on the position offered.