Description AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Are you someone who cares about customer experience, do you have a passion for Operations Management and Machine Learning? If so, then we're looking for you! The Bedrock team is a team of data linguists and associates who primarily support the training of different models in the AWS generative AI platform. We are specialized in text-based data annotation, writing for ML model training, and toxic content evaluation. Some of the aspects of ML development that the Bedrock team works with include Responsible AI, Reinforcement Learning from Human Feedback, Supervised Fine Tuning, and Human Content Evaluation. Our team represents a great array of experience in the field of linguistics, including sociolinguistics, computational linguistics, conversation analysis, syntax-semantics, linguistic typology, ESL and foreign languages, as well as translation. Key job responsibilities * Build a thorough understanding of data collection and annotation guidelines and various annotation tools. * Annotate text data, identifying linguistic categories based on detailed annotation and adhering to guidelines. * Perform annotation related tasks; you participate in data generation, collection and quality assurance tasks * Collaborate with other ML Data stakeholders to resolve data ambiguities and annotation disagreements. * Dive deep into the data to perform qualitative error trend analysis, and devise action plan to improve data quality. * Provide feedback to Language Engineers and Scientists on tool improvements and annotation processes. * Diving deep into issues and implement solutions independently * Contribute to process improvements to reduce handling time and improve resource output. * Develop a variety of language artifacts crucial for model development such as datasets for training and evaluation. * Support and consult in pre-screening interviews for Data Associates. * Collaborate with ML Data linguists (MLDL), Language Engineers (LEs) and Team Managers (TMs) to innovate processes, tracker automations, and workflows. * Assist LEs in communication with ML Data associates (MLDA) to provide detailed feedback to the annotators. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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. Why 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 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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Basic Qualifications * Bachelor's degree in a relevant field, such as Linguistics, Communications, or data-related disciplines. * At least 6 months of experience with natural language data labeling, data annotation, linguistic annotation or other forms of data markup, and/or teaching experience. * Experience identifying linguistic ambiguity and annotation inaccuracies in data. Preferred Qualifications * Familiarity with common text processing tools. * Passion for language, linguistics, human language technology and AI. * Knowledge of different domains such as Finance, Health Care, and Insurance.