Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon.
Our team is part of Amazon's Personalization organization, a high-performing group that leverages Amazon's expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personalized content recommendations, at the right time, with the right level of explanation.
As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products, and systems.
Please visit
https://www.amazon.science
for more information.
Minimum Qualifications:
PhD in CS/EE or related field, or MSc and 5+ years of applied research experience
Strong CS foundations (data structures and algorithms)
Excellent coding and design skills, proficiency with programming languages such as Java or Python
Several publications at top-tier peer-reviewed research conferences or journals
Strong communication and collaboration skills
Preferred Qualifications:
Experience in building and launching deep learning and machine learning models for business applications
Solid knowledge of big data and cloud technologies (e.g., Spark, AWS, etc.)
Experience with information retrieval, recommender systems, natural language processing, and/or personalization algorithms
Publications at top Web, Machine Learning, Natural Language Processing conferences such as KDD, ICML, NeurIPS, ACL, EMNLP, etc.
#J-18808-Ljbffr
