Senior AI Trainer

Role Title: Senior AI Trainer

Role Type: Contractor, Remote

Location: Northern America, Latin America, Europe, UK, Oceania.

micro1 is engaging Senior AI Trainers to collaborate on a customer-driven project enhancing AI system accuracy through expert-level video annotation and data labeling. In this role, you'll apply your expertise 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:

• Review video footage of robotic arms executing assigned tasks, identifying key actions and outcomes with precision.
• Apply detailed grading guidelines to accurately tag and annotate events, providing structured observations that improve model performance.
• Mark precise timestamps for the start and end of key actions, transitions, and outcomes, ensuring frame-accurate segmentation of each video session.
• Verify timestamp accuracy and alignment across annotations, correcting drift or inconsistencies between labeled events and the underlying footage.
• Utilize video annotation tools and platforms to record findings and submit annotated datasets in alignment with project standards and milestones.
• Collaborate with project trainers and contributors to resolve ambiguities, refine guidelines, and ensure annotation consistency across the team.
• Participate in ongoing quality reviews, incorporating feedback to maintain rigorous annotation standards.

Required Skills and Qualifications:

• Exceptional attention to detail and accuracy in reviewing and tagging visual data.
• 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 annotation challenges.

Preferred Qualifications:

• Proven experience with video annotation, data labeling, or similar data-centric annotation projects.
• Familiarity with video annotation tools and software platforms, including timeline-based interfaces for timestamping and event segmentation.
• Experience with frame-level or timecode-based annotation (e.g., marking event boundaries, working with frame rates, or reviewing footage frame by frame).
• Background in AI training, machine learning data preparation, or robotics projects (strongly preferred).