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:

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

Required Skills and Qualifications:

1. Exceptional attention to detail and accuracy in reviewing and tagging visual data.
2. Strong written and verbal communication skills for effective collaboration, reporting, and timely updates on progress and challenges.
3. Demonstrated time management and self-organization abilities for independent, remote project participation.
4. Analytical and problem-solving mindset, with a proactive approach to resolving annotation challenges.

Preferred Qualifications:

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