German Language Data Evaluator

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a German Language Data Evaluator based in United States.

This is a remote opportunity for a bilingual professional fluent in both German and English to contribute to AI and machine learning projects.

You’ll evaluate, label, categorize, and review language-based data to help improve the quality and performance of AI systems.

The role combines linguistic expertise, analytical thinking, quality assurance, and technology skills in a structured data environment.

You’ll work with language platforms to assess content, identify inconsistencies, and provide accurate, thoughtful evaluations.

The position offers hands-on exposure to emerging AI technologies while working with distributed teams in a fast-paced environment.

Success requires excellent attention to detail, sound judgment, adaptability, and the ability to consistently meet quality expectations.

This is an excellent opportunity for someone seeking flexible remote work while contributing directly to the development of language-focused AI technologies.

Accountabilities

• Evaluate, label, categorize, and annotate text and image data according to established project guidelines and quality standards.
• Work within a language-focused data platform to manage, review, and evaluate linguistic content accurately.
• Analyze data and identify trends, inconsistencies, or patterns that may affect project quality and performance.
• Proofread and review outputs to maintain a high level of accuracy, consistency, and relevance.
• Apply strong linguistic judgment when evaluating German and English language content.
• Provide observations, insights, and recommendations that can help improve data quality, relevance, and overall project performance.
• Manage assigned tasks efficiently while maintaining accuracy and meeting established deadlines.
• Adapt to changing project requirements, priorities, workflows, and evaluation criteria.
• Communicate effectively with team members and collaborate on quality and process-related issues.
• Navigate digital platforms and technology tools confidently to complete evaluation and annotation tasks.

Requirements

• Full professional fluency in both German and English, including excellent written and spoken communication skills.
• Must be located in the United States due to project and technical requirements.
• Must be available to work Monday through Friday during Pacific Time Zone hours.
• Strong analytical abilities and the capacity to evaluate information logically and provide well-reasoned judgments.
• Exceptional attention to detail, particularly when identifying inconsistencies, errors, or quality issues in language data.
• Strong critical-thinking and problem-solving skills with the ability to make data-informed decisions.
• Effective time-management skills and the ability to handle multiple tasks efficiently.
• Comfortable working with digital platforms, software tools, and technology-driven workflows.
• Adaptable and comfortable operating in a fast-paced environment with changing priorities.
• Strong communication and teamwork skills, with the ability to collaborate effectively in a remote setting.
• Must have valid authorization to work in the United States.
• Must have access to a personal laptop or computer suitable for completing remote work.
• Ability to successfully complete a required background check.

Benefits

• Compensation: $15-$16 per hour.
• W-2 hourly employment with benefits where applicable.
• 401(k) retirement savings plan.
• Paid time off (PTO) where applicable.
• Fully remote work from home.
• Flexible training hours designed to help you become comfortable with the role and tools.
• Entry- to mid-level opportunity offering hands-on experience in AI, machine learning, data evaluation, and language technologies.
• Opportunity to contribute to projects involving cutting-edge AI and language-model technology.
• Collaborative environment with exposure to evolving technology and data-quality practices.