Job ID: 2710278 | Amazon.com Services LLC
The Amazon Search Mission Understanding (SMU) team is at the forefront of revolutionizing the online shopping experience through the Amazon search page. Our ambition extends beyond facilitating a seamless shopping journey; we are committed to creating the next generation of intelligent shopping assistants. Leveraging cutting-edge Large Language Models (LLMs), we aim to redefine navigation and decision-making in e-commerce by deeply understanding our users' shopping missions, preferences, and goals. By developing responsive and scalable solutions, we not only accomplish the shopping mission but also foster unparalleled trust among our customers. Through our advanced technology, we generate valuable insights, providing a guided navigation system into various search missions, ensuring a comprehensive and holistic shopping experience. Our dedication to continuous improvement through constant measurement and enhancement of the shopper experience is crucial, as we strategically navigate the balance between immediate results and long-term business growth.
We are seeking an Applied Scientist who is not just adept in the theoretical aspects of Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) but also possesses a pragmatic, hands-on approach to navigating the complexities of innovation. The ideal candidate will have a profound expertise in developing, deploying, and contributing to the next-generation shopping search engine, including but not limited to Retrieval-Augmented Generation (RAG) models, specifically tailored towards enhancing the Rufus application—an integral part of our mission to revolutionize shopping assistance. You will take the lead in conceptualizing, building, and launching groundbreaking models that significantly improve our understanding of and capabilities in enhancing the search experience. A successful applicant will display a comprehensive skill set across machine learning model development, implementation, and optimization. This includes a strong foundation in data management, software engineering best practices, and a keen awareness of the latest developments in distributed systems technology. We are looking for individuals who are determined, analytically rigorous, passionate about applied sciences, creative, and possess strong logical reasoning abilities.
Join the Search Mission Understanding team, a group of pioneering ML scientists and engineers dedicated to building core ML models and developing the infrastructure for model innovation. As part of Amazon Search, you will experience the dynamic, innovative culture of a startup, backed by the extensive resources of Amazon.com (AMZN), a global leader in internet services. Our collaborative, customer-centric work environment spans across our offices in Palo Alto, CA, and Seattle, WA, offering a unique blend of opportunities for professional growth and innovation.
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 .
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/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 . This position will remain posted until filled. Applicants should apply via our internal or external career site.
Posted: July 17, 2024 (Updated about 18 hours ago)
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