Environmental AI Trainer — Remote & Data Quality Expert

Risedatalabs; United States; Remote; USD 22,000 - 39,000; Full time

Job summary

Rise Data Labs is seeking an Environmental AI Training Specialist to enhance models through expert data annotation and analysis in environmental science. You will help train AI systems with accurate, high-quality data tailored to environmental applications.

Responsibilities include annotating and labeling data, validating AI outputs, and applying domain knowledge to improve model performance across environmental sectors, with a strong emphasis on data quality and QA.

Qualifications

• Bachelor’s degree in Environmental Science, Ecology, or related field.
• Strong understanding of environmental science concepts and current trends.
• Experience in data annotation, labeling, or similar roles within AI training.
• Exceptional attention to detail and accuracy in data handling.
• Proficient in using data annotation tools and software.

Responsibilities

• Train and enhance AI models by performing data annotation and labeling specific to environmental science.
• Review and validate AI-generated content for accuracy, relevance, and adherence to environmental standards.
• Provide domain expertise to enhance AI model performance, ensuring that models reflect current environmental knowledge and practices.
• Contribute to a variety of AI training projects across different sectors within environmental science.
• Ensure high-quality data for machine learning applications, maintaining rigorous quality assurance standards.

Skills

Data annotation

Attention to detail

Data labeling

Proficient in annotation tools

Education

Bachelor’s degree in Environmental Science/ Ecology

Tools

Data annotation tools

Job description

Rise Data Labs is seeking an Environmental AI Training Specialist to enhance models through expert data annotation and analysis in environmental science. You will help train AI systems with accurate, high-quality data tailored to environmental applications.

Responsibilities include annotating and labeling data, validating AI outputs, and applying domain knowledge to improve model performance across environmental sectors, with a strong emphasis on data quality and QA.