
Job Overview
Location
California
Job Type
Full-time
Category
Data & Analytics
Date Posted
March 26, 2026
Full Job Description
📋 Description
- • The Generative AI Data Analyst role at Welocalize, Inc. is a pivotal position supporting a high-impact machine learning initiative focused on advancing large language model (LLM) capabilities through expertly crafted prompts, responses, and labeled datasets. This role directly contributes to the quality, consistency, and real-world applicability of AI systems by ensuring training data reflects nuanced, accurate, and contextually appropriate U.S. English usage across diverse domains.
- • Day-to-day responsibilities include designing and refining high-quality prompts and responses for LLM training across a wide range of topics, leading data labeling efforts in collaboration with internal teams and external partners, developing and maintaining clear annotation guidelines to ensure consistency and accuracy, conducting quality assurance reviews of labeled data, training and mentoring junior analysts on LLM data best practices, and collaborating with machine learning engineers to iterate on data strategies based on model performance feedback.
- • Welocalize, Inc. is a global leader in language and content solutions, specializing in helping enterprises adapt their products and services for international markets through AI-powered localization, translation, and data enrichment. The company combines deep linguistic expertise with cutting-edge technology to support innovation in AI, particularly in multilingual and culturally adaptive machine learning systems.
- • In this role, the analyst will develop advanced skills in prompt engineering, data labeling methodologies for generative AI, LLM evaluation frameworks, and cross-functional collaboration in AI development pipelines. They will gain hands-on experience shaping the training data that powers next-generation AI models, positioning them as a specialist in the rapidly growing field of AI data operations and ethical AI development.
🎯 Requirements
- • Native-level proficiency in U.S. English with strong written and verbal communication skills
- • Experience working with data, including data labeling, annotation, or quality assurance in AI/ML contexts
- • Ability to train and guide teams on best practices for LLM data creation and consistency
- • Comfortable working remotely and managing time effectively in a self-directed environment
- • Valid work authorization in the United States (W-2 employment only; no visa sponsorship)
🏖️ Benefits
- • Fully remote position with flexible scheduling within a 40-hour workweek
- • Competitive hourly pay rate of $36/hour
- • Comprehensive benefits package as a full-time W-2 employee
- • Opportunity to work on cutting-edge generative AI projects with real-world impact
- • Professional growth in AI data operations, prompt engineering, and LLM training methodologies
Skills & Technologies
About Welocalize, Inc.
Welocalize, Inc. is a global language services and technology company that provides translation, localization, and content transformation solutions. Founded in 1997 and headquartered in Frederick, Maryland, the company helps enterprises adapt products, marketing, support, and digital experiences for more than 250 languages. Services include technical translation, multilingual SEO, machine-translation post-editing, data collection, linguistic testing, and AI training data. Welocalize operates a worldwide network of linguists and engineers, supported by proprietary automation and quality-management platforms. Clients span technology, life sciences, finance, manufacturing, and consumer sectors, enabling international growth through culturally relevant, compliant, and scalable multilingual content.
Subscribe to the weekly newsletter for similar remote roles and curated hiring updates.
Newsletter
Weekly remote jobs and featured talent.
No spam. Only curated remote roles and product updates. You can unsubscribe anytime.
Similar Opportunities

Airwallex (UK) Limited
1 month ago

Airwallex (UK) Limited
2 months ago
