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Data Annotation For Ai Jobs (NOW HIRING)

BC Forward

AI Data Annotation Specialist

Charlotte, NC · On-site

$65.05/hr

Develop evaluation frameworks for RAG systems, including retrieval metrics, grounding quality, and factual consistency. Identify and mitigate hallucinations, context drift, routing errors, and ...

Sigma Group

Tamil Translator (Remote) | Sigma AI

$45K - $58K/yr

AI - Shaping the Future of Artificial Intelligence What is Sigma ... Sigma is a leading global technology company specializing in data collection and annotation for ...

Weekday AI

General Counsel

$80 - $105/hr

Collaborate with product and research teams to refine training data, annotation frameworks, and best practices for AI-driven contract review systems * Contribute to the development of high-quality ...

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Data Annotation For Ai information

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

How much do AI data annotators make?

AI data annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Some positions may offer freelance or project-based pay, with rates varying accordingly.

Is data annotation AI job real?

Yes, data annotation for AI is a real job that involves labeling data such as images, text, or videos to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and roles can be found in tech companies and AI development environments.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

Can you use data annotation for AI?

Data annotation for AI involves labeling and categorizing data such as images, text, or audio to train machine learning models. Data annotation jobs require attention to detail and often involve using specialized tools or platforms; they are essential for developing accurate AI systems.

What does an AI data annotator do?

An AI data annotator labels and tags data such as images, videos, text, or audio to help train machine learning models. They use specialized tools to ensure data is accurately annotated according to project guidelines, which is essential for developing effective AI systems.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
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Infographic showing various Data Annotation For Ai job openings in the United States as of June 2026, with employment types broken down into 84% Full Time, 12% Part Time, 3% Temporary, and 1% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.
Atlas Data Operations Annotation Manager

Atlas Data Operations Annotation Manager

Boston Dynamics

Waltham, MA • On-site

$115K - $140K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

Boston Dynamics is a world leader in mobile robots, tackling some of the toughest robotics challenges. We combine the principles of dynamic control and balance with sophisticated mechanical designs, cutting-edge electronics, and next-generation software for high-performance robots equipped with perception, navigation, and intelligence.
The Atlas team is focused on advancing machine learning and manipulation capabilities. We are seeking an Annotation Manager to own data quality and annotation operations for Atlas - setting standards, leading the team, and managing the vendor relationships that produce the training data behind Atlas's AI systems. This role spans software quality assurance, data quality strategy, and hands-on operational leadership, and reports to the Atlas Data Operations Associate Director.
Schedule/Working Hours:
  • Monday - Friday: 40 hours per week, regular hours (e.g., 9 AM to 6 PM, with flexibility across both first and second shift as required).

Responsibilities:
Team Leadership & Performance
  • Directly manage a team of Annotation Leads and Annotation QA Leads, providing day-to-day direction, prioritization, coaching, and performance feedback.
  • Serve as the Atlas working team lead for third-party annotation vendors, managing task allocation, performance accountability, and dual-source relationships to optimize cost, quality, and speed.

Annotation Quality & Operations
  • Own data quality end-to-end - defining standards, QA methodologies, and metrics (accuracy, consistency, rework rates, guideline adherence) - and serve as the primary escalation point for edge cases and labeling ambiguities.
  • Write SOPs and technical documentation for the annotation team and vendors; forecast annotation needs across Atlas engineering stakeholders and drive continuous improvement across tooling, workflows, and guidelines.

Required Skills & Experience:
  • Bachelor's degree in a technical field, data science, or cognitive science preferred; proven experience managing teams in a data annotation, data quality, or machine learning data pipeline environment preferred.
  • Prior experience managing business/contractual relations between third-party annotation vendors/labelling service or BPO service providers strongly preferred. Familiarity with ML data pipelines preferred. Exceptional organizational and communication skills required.

The base pay range for this position is between $115,000 to $140,000 annually. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and an annual bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer for employment.

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