- अनुभव
- 2 वर्ष तक
- वेतन
- —
- उद्घाटन
- 1
- की तैनाती
- 2 सप्ताह पहले
- कार्य मोड
- कार्यालय में हूँ
- पात्रता
- Recent graduates or early-career professionals with 0 to 2 years of relevant experience in annotation, moderation, research, editorial, or similar work.
- फिर शुरू करना
- आवेदन करना आवश्यक है
आप कहाँ काम करेंगे
नौकरी का विवरण
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.