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Amazon

Research Scientist, Artificial General Intelligence - Data Services

Amazon, Boston, Massachusetts, us, 02298


AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us.

We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists.

Key Job Responsibilities

Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.

Clean, analyze and select speech/language/other data to achieve goals.

Build and test models that elevate the customer experience.

Collaborate with colleagues from science, engineering and business backgrounds.

Present proposals and results in a clear manner backed by data and coupled with actionable conclusions.

Work with engineers to develop efficient data querying infrastructure for both offline and online use cases.

Minimum Qualifications

PhD, or Master's degree and 4+ years of quantitative field research experience.

Experience investigating the feasibility of applying scientific principles and concepts to business problems and products.

Experience in Python, Perl, or another scripting language.

Experience with various machine learning techniques and parameters that affect their performance.

Experience analyzing both experimental and observational data sets.

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