Specific responsibilities include
Design and develop comprehensive evaluation and benchmarking datasets for Quick Suite AI-powered features
Leverage LLMs for synthetic data corpora generation; data evaluation and quality assessment using LLM-as-a-judge settings
Create ground truth datasets with high-quality question-answer pairs across diverse domains and use cases
Lead human annotation initiatives and model evaluation audits to ensure data quality and relevance
Develop and refine annotation guidelines and quality frameworks for evaluation tasks
Conduct statistical analysis to measure model performance, identify failure patterns, and guide improvement strategies
Collaborate with ML scientists and engineers to translate evaluation insights into actionable product improvements
Build scalable data pipelines and tools to support continuous evaluation and benchmarking efforts
Contribute to Responsible AI initiatives by developing safety and fairness evaluation datasets
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
About AWS
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Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Work/Life Balance
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Mentorship & Career Growth
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2+ years of data scientist experience
3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
1+ years of working with or evaluating AI systems experience
Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
Knowledge of machine learning concepts and their application to reasoning and problem-solving
Experience in Python, Perl, or another scripting language
Experience in a ML or data scientist role with a large technology company
Experience in defining and creating benchmarks for assessing GenAI model performance
Experience working on multi-team, cross-disciplinary projects
Experience applying quantitative analysis to solve business problems and making data-driven business decisions
Experience effectively communicating complex concepts through written and verbal communication
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