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Data Annotation For Ai Jobs (NOW HIRING)

Encord

Human Data Operations Strategist

San Francisco, CA · On-site

$150K - $230K/yr

Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams * Ensure the highest standards of data ...

GE HealthCare

Data Manager - AI Development

$94K - $141K/yr

This role works closely with AI/ML engineers to define data needs for AI features, coordinates with internal and external data collection teams/clinical team, oversees annotation activities, and ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site +1

... for artificial intelligence (AI) and machine learning (ML). Q Analysts is headquartered in San Jose, CA with a presence throughout the US and internationally. The company has been recognized ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site

... for artificial intelligence (AI) and machine learning (ML). Q Analysts is headquartered in San Jose, CA with a presence throughout the US and internationally. The company has been recognized ...

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Data Annotator (Autonomous Vehicle / AI)

San Francisco, CA · On-site

$32 - $39/hr

Data Annotator (Autonomous Vehicle / AI) Location: San Francisco, CA 94103 Schedule: Hybrid, Monday ... annotation, and understands the importance of precision in data labeling for real-world ...

Mercor

Network Engineer - Data Annotation

New York, NY · Remote

$50 - $70/hr

Position: Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation ... AI interview based on your resume * Submit form Resources & Support * For details about the ...

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All JobsData Annotation For Ai Jobs

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.

Human Data Operations Strategist

Encord

San Francisco, CA • On-site

$150K - $230K/yr

Full-time

Medical, Dental, Vision, PTO

Posted yesterday

Job description

About us
Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.
Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.
The role
As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and machine learning workflows for our clients. You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models.
What you'll do
  • Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams
  • Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops
  • Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process
  • Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices
  • Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies

Who we're looking for
  • A sharp, execution-oriented operator with a consulting or AI company pedigree - you bring structured thinking, strong project management instincts, and a bias for getting things done
  • Analytically rigorous and comfortable with ambiguity - you break down complex operational challenges from first principles and build clear, actionable plans to solve them
  • Technically fluent enough to get hands-on with data - whether that's querying a database, auditing annotation outputs, or automating a workflow in Python
  • Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance
  • A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track
  • Entrepreneurial and collaborative - you thrive in fast-paced environments and take ownership without waiting to be told what to do

Experience requirements
  • 3-7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies
  • Proven ability to own complex, multi-stakeholder workflows end-to-end - from scoping and planning through execution, quality assurance, and iteration
  • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued
  • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability - ideally in a context involving human-in-the-loop workflows or structured labelling tasks
  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions
  • Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams

Why Encord
  • Competitive salary, commission, and meaningful equity in a high-growth start-up
  • Clear, accelerated growth opportunities as the company scales rapidly
  • Strong in-person culture: 3-5 days/week in our newly launched North Beach loft office
  • Flexible PTO to fully recharge
  • 18 paid vacation days in the U.S. plus federal holidays
  • Annual learning & development budget
  • Comprehensive health, dental, and vision coverage
  • Frequent travel opportunities across the U.S., London, and Europe
  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials

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