Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? 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, generative AI, large-scale data 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 and up-to-date recommendations that are related to their interests. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future.
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, information retrieval 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.
- MSc in CS/EE or related field and 2+ years of applied research experience
- Strong CS foundations (data structures and algorithms)
- Excellent coding and design skills, proficiency with Python
- Publication at top-tier peer-reviewed research conferences or journals
- Strong communication and collaboration skills
- Hands-on Experience with bringing LLM systems to production
- 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.
