Hire this AI Trainer
Sign in or create an account to invite AI Trainers to your job.
No software listed
No data types listed
No task types listed
I have hands-on experience in AI training and data labeling, primarily focused on preparing high-quality datasets for supervised machine learning models. My work has involved annotating text, images, and audio data across multiple domains, including natural language processing (sentiment analysis, entity recognition, and intent classification) and computer vision (bounding boxes, segmentation, and landmark labeling). I am skilled in using labeling platforms like Labelbox, Supervisely, and custom in-house tools, and I have consistently followed strict QA protocols to ensure inter-annotator agreement and data consistency. I’ve also contributed to refining labeling guidelines by providing feedback on edge cases, which helped reduce ambiguity and improve model performance in subsequent training cycles. Beyond execution, I’ve taken on coordination and quality assurance roles, where I reviewed annotated data, resolved discrepancies, and managed small teams of annotators to meet project deadlines. I’m comfortable working with iterative feedback loops—retagging data based on model predictions and actively participating in error analysis sessions to identify annotation drift or blind spots. This experience has given me a strong appreciation for the critical role that clean, well-structured data plays in model development, as well as the patience and attention to detail required to produce it at scale. I’m also familiar with basic scripting (Python) to automate repetitive validation checks, which has improved efficiency without compromising quality.
I have a high school diploma and am currently learning the fundamentals of AI and data labeling through free online courses. I have also completed training modules on data ethics and image annotation
I have experience working on data quality and annotation projects, where I labeled and reviewed datasets used to train m
