As part of Alexa CAS team, our mission is to provide scalable and reliable evaluation of the state-of-the-art Conversational AI. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), to invent and build end-to-end evaluation of how customers perceive state-of-the-art context-aware conversational AI assistants.
A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc.
As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel methods for evaluating conversational assistants. You will analyze and understand user experiences by leveraging Amazon’s heterogeneous data sources and build evaluation models using machine learning methods.
Key job responsibilities
- Design, build, test and release predictive ML models using LLMs
- Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.
- Collaborate with colleagues from science, engineering and business backgrounds.
- Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions
- Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use cases
About the team
Central Analytics and Research Science (CARS) is an analytics, software, and science team within Amazon's Conversational Assistant Services (CAS) organization. Our mission is to provide an end-to-end understanding of how customers perceive the assistants they interact with – from the metrics themselves to software applications to deep dive on those metrics – allowing assistant developers to improve their services. Learn more about Amazon’s approach to customer-obsessed science on the Amazon Science website, which features the latest news and research from scientists across the company. For the latest updates, subscribe to the monthly newsletter, and follow the @AmazonScience handle and #AmazonScience hashtag on LinkedIn, Twitter, Facebook, Instagram, and YouTube.
A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc.
As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel methods for evaluating conversational assistants. You will analyze and understand user experiences by leveraging Amazon’s heterogeneous data sources and build evaluation models using machine learning methods.
Key job responsibilities
- Design, build, test and release predictive ML models using LLMs
- Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.
- Collaborate with colleagues from science, engineering and business backgrounds.
- Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions
- Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use cases
About the team
Central Analytics and Research Science (CARS) is an analytics, software, and science team within Amazon's Conversational Assistant Services (CAS) organization. Our mission is to provide an end-to-end understanding of how customers perceive the assistants they interact with – from the metrics themselves to software applications to deep dive on those metrics – allowing assistant developers to improve their services. Learn more about Amazon’s approach to customer-obsessed science on the Amazon Science website, which features the latest news and research from scientists across the company. For the latest updates, subscribe to the monthly newsletter, and follow the @AmazonScience handle and #AmazonScience hashtag on LinkedIn, Twitter, Facebook, Instagram, and YouTube.