AI Data Specialist - United States

About the role

This position centers on driving the quality and safety of AI-generated English content through meticulous data operations. The hire will own the evaluation and refinement of model outputs to ensure they meet defined standards of accuracy and relevance. You will own the process of pairing and comparing different responses to identify subtle differences in model behavior. Ownership of object tagging and labeling will extend across varied media, including audio, video, and static images. The role involves systematic counting and verification tasks to measure data quality and model consistency. You will directly influence the development and safety of today's AI models through careful assessment and annotation. This is a critical function in shaping how future AI systems handle language and generate reliable content. Your work will provide the necessary insights to improve model performance and user trust.

Key facts

What you'll do

Perform comprehensive data collection, evaluation, and annotation across diverse digital content. This core responsibility ensures that source materials are properly sourced, reviewed, and prepared for model training and evaluation.

Execute pairwise comparisons between data sets to assess quality and relevance differences. You will systematically compare responses to identify subtle differences in model behavior and output quality.

Conduct detailed counting tasks to verify volume, completeness, and distribution of data elements. These tasks are essential for maintaining dataset integrity and balance.

Carry out object tagging and labeling for audio, video, image, and text-based materials. You will apply structured frameworks to categorize content across multiple media types accurately.

Monitor and assess AI-generated content to determine accuracy, coherence, and factual validity. This evaluation feeds directly into quality improvement initiatives.

Identify and categorize specific data types to support targeted model training and refinement. Proper categorization ensures that models learn from appropriately structured inputs.

Apply quality assurance measures to ensure annotated data meets strict consistency standards. You will implement checks that guarantee high-quality outputs for downstream model training.

Filter and organize collected data to create structured inputs for machine learning workflows. This structuring enables efficient and effective model training processes.

Document observations and patterns found during evaluation to guide iterative model improvements. Your insights will drive evidence-based refinements to AI systems.

Support the development of safety protocols by flagging problematic content and edge-case scenarios. This contributes directly to the responsible deployment of AI technologies.

Collaborate with internal teams to align annotation guidelines with evolving project objectives. Your work will support shifting priorities and emerging requirements.

Maintain detailed records of all processed data to ensure traceability and reproducibility of results. This documentation supports auditability and long-term model validation.

Requirements

Fluent or advanced proficiency in English at levels B2-C2 is mandatory for this role. This language requirement is essential for performing all evaluation and annotation tasks accurately.

Applicants must possess experience in machine learning tasks, data collection and preprocessing, data evaluation and quality assurance, and data annotation and labeling. This background ensures you can handle the technical demands of the position.

Eligibility requires the capability to perform tasks involving object tagging and labeling across audio, video, images, or collected data. You must be comfortable working with multimodal content types.

You must demonstrate consistent performance in data collection, evaluation, and annotation activities. Reliability is key to maintaining dataset quality and project momentum.

The role requires handling complex content types and applying structured labeling frameworks accurately. Attention to detail is critical when working with intricate classification schemes.

Reliable execution of counting tasks and pairwise comparisons is essential for success in this position. These methods provide quantitative insights into model behavior and data quality.

Strict adherence to quality standards ensures that annotated data remains useful for AI model development. Your commitment to these standards directly impacts model performance.

Only candidates with proven AI and data capabilities in relevant domains should apply for this opportunity. This requirement ensures alignment between candidate expertise and role expectations.

Nice to have

Preferred experience in one or more areas such as machine learning tasks, data collection and preprocessing, data evaluation and quality assurance, and data annotation and labeling.

Practical notes

This is a part-time engagement requiring 10 or more hours per week. The schedule is flexible, allowing work to be performed at times that suit the individual.

The role is conducted remotely, enabling work from home. Payment is issued on a timely basis at a rate of 15 USD per hour.

The position is ideal for students, recent graduates, stay-at-home parents, gig workers, or other professionals seeking supplemental income.

Start date is immediate, with duration to be confirmed.

This opportunity is open to individuals located in the state of Texas.