Data Annotator

Optimal Staffing; Dearborn (MI); In loco; USD 21.000 - 34.000; Tempo pieno

Descrizione del lavoro

Optimal Staffing is seeking a Data Annotator to improve AI responses by labeling conversational data, including text and audio, in line with OEM guidelines. The role demands accuracy, attention to detail, and the ability to document labeling decisions and participate in calibration sessions.

Applicants should have experience labeling conversational AI data, strong written and spoken English, and the ability to produce weekly labeling-productivity reports. Automotive experience is a plus.

Competenze

• Experience labeling conversational AI data (text and audio).
• Prior automotive industry experience preferred.
• Professional fluency in English with strong reading comprehension and written communication skills.

Mansioni

• Label conversational AI data following guidelines.
• Review chatbot conversations, responses, and related artifacts.
• Maintain records of annotations, quality results, exceptions, escalations, and open issues.
• Prepare weekly reports on labeling volume, productivity, quality results, backlog, and open questions.
• Participate in calibration sessions and onboarding to align with evolving guidelines.

Conoscenze

Analytical

Communications

Data annotation

Descrizione del lavoro

Data Annotator Improve the quality of the chatbot responses by creating a feedback loop where AI responses are judged by humans. Review chatbot conversations and related artifacts including individual turns, model responses, retrieved content, citations, and metadata within OEM approved tools. Apply OEM approved labeling guidelines, taxonomies, and decision rules, completing all required labels, rationales, confidence assessments, and ambiguity indicators. Evaluate chatbot performance across dimensions defined by OEM, including intent, task completion, correctness, completeness, relevance, groundedness, source attribution, response quality, safety, policy compliance, escalation need, and failure category. Identify and elevate content that is ambiguous, sensitive, safety-relevant, outside the current guidelines, or requires subject-matter-expert review. Labelers must not infer or create standards where approved guidance does not exist. Participate in onboarding, calibration, refresher training, quality reviews, and adjudication sessions to maintain consistent labeling decisions. Conduct first-level quality checks on assigned work and correct errors identified through OEM or supplier quality reviews. Maintain complete and accurate records of completed annotations, quality results, exceptions, escalations, and open issues in OEM-designated systems. Provide weekly reports summarizing completed volume, productivity, quality results, backlog, open questions, risks, guideline gaps, and required actions. Recommend and document improvements to the annotation guidelines and taxonomy based on recurring ambiguities, labeling patterns, and adjudication outcomes. Changes may be implemented only after OEM approval.

Skills Required:

Analytical, Communications, Data Annotation - Voice and Text

Experience Required:

Experience labeling conversational AI data, including text and audio.

Prior experience in the automotive industry is preferred.

Professional fluency in English, with strong reading comprehension and written communication skills. Demonstrated ability to interpret and consistently apply detailed guidelines, taxonomies, and decision rules.

Strong analytical judgment and attention to detail, including the ability to identify factual errors, incomplete responses, unsupported claims, policy concerns, and ambiguous cases.

Ability to document labeling decisions and provide clear, concise rationales.

Ability to work accurately on repetitive tasks while meeting quality, productivity, and turnaround expectations. Ability to participate in calibration sessions, incorporate feedback, and adjust decisions as guidelines evolve.

Ability to prepare clear weekly reports summarizing labeling volume, productivity, quality results, progress, and open issues.