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Data Annotation For Ai Jobs (NOW HIRING)

Encord

Human Data Solutions Engineer

San Francisco, CA · On-site

... AI requirements into clear instructions for annotation specialists, auditing outputs, and iterating on quality until the sample is client-ready • Build and deliver tailored demonstrations that ...

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 ...

Abaka AI

Data Acquisition Engineer

Mountain View, CA · On-site

$136.30K - $163.60K/yr

Abaka AI is built on one mission: to be the world's most trusted data partner for AI companies. As ... annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with ...

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Data Annotation For Ai information

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.

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.

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.

How to become an AI data annotator?

To become an AI data annotator, you typically need strong attention to detail, good communication skills, and familiarity with annotation tools or platforms. Many roles require a high school diploma or equivalent, and some may prefer experience with specific data types like images, text, or audio. Training is often provided by employers, and the work can be part-time or flexible, depending on the company.

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.

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Infographic showing various Data Annotation For Ai job openings in the United States as of May 2026, with employment types broken down into 91% Full Time, 3% Part Time, and 6% Contract. Highlights an 91% Physical, 7% Hybrid, and 2% Remote job distribution.

Human Data Solutions Engineer

Encord

San Francisco, CA • On-site

Full-time

Posted 4 days ago

Job description

Job Summary:
Encord is the universal data layer for AI that helps AI teams train and run models on the right data. The Human Data Operations & Solutions Engineer will manage technical sales and hands-on data operations, leading the delivery of proof of concepts and ensuring high-quality data annotation workflows.
Responsibilities:
• Partner with Account Executives to lead the technical and operational strategy for complex enterprise sales cycles, co-owning the path to a successful proof of concept
• Lead deep technical discovery sessions with ML Engineers, MLOps leaders, and non-technical stakeholders to understand data requirements and design the right annotation workflow
• Manage end-to-end delivery of small-scale annotation POCs — translating complex AI requirements into clear instructions for annotation specialists, auditing outputs, and iterating on quality until the sample is client-ready
• Build and deliver tailored demonstrations that combine platform capability with live, real-world annotation results — particularly for robotics, autonomous driving, and multimodal sensor data (LiDAR, camera fusion, etc.)
• Act as a trusted advisor to clients on annotation workflow design, data quality, and the operational processes that underpin model performance
• Provide structured feedback and guidance to annotation teams during POC delivery, ensuring outputs meet the quality bar required to win client confidence
• Translate findings and operational results into clear value propositions for senior, non-technical stakeholders
• Serve as the voice of the customer to Product and Engineering, channelling detailed technical feedback from enterprise clients to shape the roadmap
Qualifications:
Required:
• 1-3 years of professional experience, ideally spanning strategy consulting, AI/technology operations, or customer-facing technical roles (Solutions Engineering, Technical Account Management, or similar)
• Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and client communication
• Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs
• Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally involving human-in-the-loop or structured labelling workflows
• Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients
• Hands-on experience with at least one major cloud platform (GCP, AWS, or Azure), including data storage and ML workflow patterns
Preferred:
• Bonus: hands-on experience with computer vision, LiDAR, robotics sensor data, or autonomous driving datasets
• Prior exposure to data annotation platforms or quality management frameworks
• Experience in a customer-facing technical role at an AI company
Company:
Encord is the AI native data Infrastructure company. We help teams curate, manage, and annotate the data needed to train and run AI Founded in 2021, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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