
Marvelous Ajibade
Ai data trainer
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About
Detail-oriented AI Data Annotator and AI Trainer with hands-on experience delivering Tier 2–3 quality annotations on large-scale computer vision and multimodal datasets using Atlas and related annotation platforms. Proven ability to follow strict client guidelines, identify edge cases, and reduce major/minor errors while maintaining high throughput and consistency. Also experienced in training and evaluating AI models at Micro1, supporting model accuracy, safety, and alignment through high-quality human feedback.
Complemented by foundational knowledge in Cybersecurity Governance, Risk, and Compliance (GRC), bringing a strong understanding of data privacy, compliance, and quality assurance to AI workflows.
Experience
AI Data Annotator (Atlas Platform) at Contract / Remote (2025-12 – Present)
Performed high-accuracy data annotation on large-scale datasets using Atlas, meeting strict client-defined guidelines and quality thresholds.
- Delivered Tier 2 and Tier 3 annotations, handling complex tasks including temporal action segmentation, fine-grained object and hand-action labeling, and dense vs. coarse action consistency
- Identified and corrected major and minor annotation errors, including missed task-relevant actions, incorrect object identification, and timestamp misalignment and segmentation errors
- Maintained consistent wording and structure across annotation episodes to ensure dataset reliability
- Demonstrated strong ability to interpret evolving guidelines and apply updates accurately without supervision
- Consistently met or exceeded productivity and accuracy benchmarks
- Key Tools: Atlas, internal QA tools, annotation dashboards
AI Trainer / AI Quality Contributor at Micro1 (2026-01 – Present)
Contributed to training and improving AI models through high-quality human feedback and data labeling.
- Evaluated AI outputs for accuracy, relevance, safety, and alignment, providing structured corrections and improvements
- Supported model learning by supplying edge cases and nuanced examples to reduce hallucinations and logical errors
- Followed strict quality and confidentiality requirements while working on sensitive AI training tasks
- Collaborated asynchronously with global teams to maintain consistent annotation and feedback standards