ML Data Associate III, AWS Quick

• Design and execute linguistic test cases to validate Quick's natural language features across diverse inputs, edge cases, and user scenarios.
• Evaluate model outputs for accuracy, fluency, and relevance by applying linguistic expertise to identify regressions, hallucinations, and quality gaps.
• Develop and maintain testing guidelines and rubrics that ensure consistent, repeatable evaluation standards across annotators and test cycles.
• Analyze test results and report findings using Python and SQL to surface patterns, quantify defect rates, and prioritize issues for engineering teams.
• Collaborate with engineers, scientists, and PMs to define acceptance criteria, triage language-related bugs, and validate fixes before releases
• Work on high complex annotations and audits as needed
• Work effectively both independently and as part of a team
• Uphold a high bar for quality while delivering accurate work at pace
• Apply strong attention to detail and Critically assess data quality
• Manage competing tasks and adapt quickly to shifting priorities
• Take ownership and Exercise sound judgment in escalating
• Handle highly complex tasks and Scale with growing workloads

• 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
• Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

• Inclusive Team Culture
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• Work/Life Balance
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• Mentorship & Career Growth
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Basic qualifications

• - 3+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
• - Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
• - Completed a bachelor’s degree in engineering, Computer Science, Machine Learning, Operations Research, Data Science/ Linguistics
• - Knowledge of linguistic fundamentals, including handling ambiguity, natural language processing concepts, and query (Q&A) interpretation

Preferred qualifications

• - Experience programming in Python or a related language
• - Proficient in SQL

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