Data Labeling Specialist (LLM Training)

About the job

Job Overview

We are seeking detail-oriented and intellectually sharp Data Labeling Specialists to join our AI team and contribute to the creation of high-quality datasets for Large Language Model (LLM) training. You will be responsible for annotating and evaluating task-driven natural language data across various capabilities such as instruction understanding, retrieval, answer generation, and end-to-end problem solving. Your work will directly influence the accuracy, usefulness, and safety of our AI systems.

Salary Range: $300 to $500 per month depending on the experience.

Key Responsibilities

A.) Text Annotation & Task Labeling

Perform high-quality annotation on a wide range of NLP tasks, including but not limited to:

Intent Classification

Text Rewriting / Rephrasing

Category Classification

Retrieval Tasks – including vector database queries, tool invocation, and web search result evaluations

Answer Generation

End-to-End Tasks – such as multi-turn conversations or complex instruction resolution

Understand task context and label data precisely according to the objective and output requirements.

B.) Instruction Tuning & Output Evaluation

Create and assess high-quality instruction-response pairs for use in fine-tuning LLMs.

Evaluate AI-generated responses across multiple dimensions (e.g., correctness, helpfulness, completeness, logical flow), and provide structured feedback or improved examples.

C.) Quality Review & Control

Participate in calibration sessions to ensure consistency in labeling style and logic.

Review other annotators’ work, identify issues, and suggest improvements.

Detects and flag inappropriate, unsafe, repetitive, or low-quality data for filtering.

D.) Workflow Collaboration & Optimization

Work closely with model engineers and data managers to improve labeling processes and tools.

Provide suggestions to refine annotation guidelines, prompts, and evaluation rubrics to enhance efficiency and precision.

Requirements

Must-Have:

Excellent written and reading comprehension in English.

Strong attention to detail and logical reasoning skills for handling complex and evolving tasks.

Experience in NLP, linguistics, or previous data labeling work.

Familiarity with AI-generated content and basic understanding of LLM behavior (e.g., ChatGPT, Claude, Gemini).

Nice-to-Have:

Experience with annotation platforms such as Prodigy, Label Studio, Scale AI, or SageMaker Ground Truth.

Familiarity with retrieval-based systems, vector databases, or tool-augmented LLM pipelines.

Experience in crypto space.

What You’ll Gain

First-hand experience contributing to the next generation of LLMs.

Exposure to diverse and advanced real-world LLM training workflows.

A collaborative, interdisciplinary, and multilingual team driving real AI impact.

Industries:Information Technology & Services, Internet

Function: Others