Data Annotator

About the Role

Jobright is a modern AI-focused job search platform that aims to make career discovery quicker, more intelligent, and more tailored to each user. The team is hiring a Data Annotator to build precise, high-quality datasets that help train AI agents to interpret resumes, understand hiring trends, and support people in finding stronger career opportunities.

Why This Role Matters

• Your annotation choices will serve as the reference standard that shapes how AI agents perform in live production environments.
• You’ll be able to see dataset improvements translate into better agent quality from one iteration to the next.
• The role offers close collaboration with applied AI and research teams, along with potential growth toward AI engineering or machine learning roles in the future.

Key Responsibilities

• Label and assess resumes, job ads, agent conversation logs, and other text-based materials used to train and refine AI systems.
• Handle unclear cases thoughtfully, identify recurring patterns or gaps, and help improve guidelines to reduce mistakes over time.
• Partner with applied AI engineers to pinpoint where agents are underperforming and create focused annotation work to address those weaknesses.
• Help maintain annotation playbooks, quality checks, and reviewer processes so output stays reliable as the team and dataset expand.

What We’re Looking For

The ideal candidate is a recent graduate or someone early in their career with 0 to 2 years of relevant experience in annotation, content moderation, research, editorial work, or a similar area.

You should communicate clearly, explain the reasoning behind labeling decisions, and ask strong questions when instructions do not fully address a scenario.

A careful approach, strong attention to detail, and comfort making decisions in uncertain situations are important, along with a basic understanding of how labeled data affects AI and machine learning behavior.

Preferred Background

• Experience through an internship or project in data annotation, linguistics, qualitative research, or content operations, especially within a technology or AI-oriented company.
• Ability to work through large amounts of complex material accurately, even when the topic is unfamiliar at first.
• Exposure to annotation tools such as Label Studio or Scale, plus basic SQL or spreadsheet skills and experience reviewing LLM outputs or prompt-driven workflows.