Senior AI Trainer

Role Title: Senior AI Trainer Role Type: Contractor, Remote Location: Northern America and Europe. micro1 is engaging Senior AI Trainers to collaborate on a customer-driven project enhancing AI system quality through expert-level model training and evaluation. In this role, you'll apply your generalist expertise and hands-on experience with AI products to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. Key Responsibilities: - Evaluate the performance of AI chat and search tools under standardized, repeatable conditions. - Apply detailed grading guidelines to accurately evaluate, rank, and annotate model responses, providing structured feedback that improves model performance. - Write, refine, and stress-test prompts that probe model capabilities, ensuring coverage of diverse scenarios, edge cases, and user intents. - Verify consistency and accuracy across evaluations, correcting discrepancies between ratings and the underlying rubric or guidelines. - Utilize AI training tools and platforms to record findings and submit evaluation datasets in alignment with project standards and milestones. - Collaborate with project trainers and contributors to resolve ambiguities, refine guidelines, and ensure evaluation consistency across the team. - Participate in ongoing quality reviews, incorporating feedback to maintain rigorous training and annotation standards. Required Skills and Qualifications: - Exceptional attention to detail and accuracy in reviewing and evaluating AI-generated content. - Strong written and verbal communication skills for effective collaboration, reporting, and timely updates on progress and challenges. - Demonstrated time management and self-organization abilities for independent, remote project participation. - Analytical and problem-solving mindset, with a proactive approach to resolving evaluation challenges. Preferred Qualifications: - Proven experience with AI model training, RLHF, data labeling, or similar data-centric evaluation projects. - Familiarity with AI products and tools, including prompt engineering and understanding of model behaviors and limitations. - Experience writing evaluation rubrics, grading model outputs, or reviewing AI responses for accuracy, helpfulness, and safety. - Background in AI training, machine learning data preparation, or LLM-focused projects (strongly preferred). Originally posted on Himalayas