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Ai Data Annotation Remote Jobs (NOW HIRING)

Abaka AI

Quality Project Associate

Responsibilities : • Build and improve quality assurance and compliance systems across AI data annotation projects • Design quality standards, review processes, escalation workflows, and ...

Akidev Corporation

Data Engineer III 70756-1

Menlo Park, CA · On-site +1

$134K - $162K/yr

... AI-augmented, large-scale data pipelines (billions of images) integrating traditional transformations with ML models (classifiers, embeddings, LLMs) for cleaning and annotation. Remote Inference ...

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What is an AI Data Annotation Remote job?

An AI Data Annotation Remote job involves labeling, tagging, or categorizing data used to train artificial intelligence models. Annotators work with text, images, audio, or video to ensure machine learning algorithms receive accurate and high-quality input. This role is performed remotely, allowing flexibility in work location and schedule. Attention to detail, consistency, and familiarity with annotation tools are essential skills for this job.

What are the key skills and qualifications needed to thrive in the Ai Data Annotation Remote position, and why are they important?

To thrive as an AI Data Annotation Remote worker, you need strong attention to detail, familiarity with data labeling processes, and a basic understanding of machine learning concepts, often supported by a high school diploma or relevant experience. Familiarity with data annotation platforms such as Labelbox, Supervisely, or AWS SageMaker Ground Truth is typically required, and certifications in data annotation or AI may be advantageous. Strong time management, the ability to work independently, and clear communication skills are valuable in this remote role. These abilities ensure accurate and efficient data labeling, which is critical for training reliable AI models.

What does a typical day look like for someone working remotely in AI data annotation?

A typical day for a remote AI Data Annotation worker involves reviewing and labeling various types of data such as images, text, or audio according to specific guidelines provided by the employer or project lead. You may use specialized annotation software and work through batches of data while following quality standards and deadlines. Periodic team check-ins or virtual meetings help clarify instructions, address questions, and monitor progress. While most of the work is independent, communication with supervisors or quality assurance teams is important to ensure that data labeling is consistent and accurate.
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Ai Data Annotation Remote jobs near you
Infographic showing various Ai Data Annotation Remote job openings in the United States as of May 2026, with employment types broken down into 71% Full Time, and 29% Part Time. Highlights an 100% Remote job distribution.

Quality Project Associate

Abaka AI

Remote

Full-time

Posted 3 days ago

Job description

Job Summary:
Abaka AI is a trusted data partner for AI companies, focusing on providing scalable data solutions. The Quality Project Associate will build and scale quality systems for AI data operations, working closely with various teams to improve data quality and compliance in large-scale annotation programs.
Responsibilities:
• Build and improve quality assurance and compliance systems across AI data annotation projects
• Design quality standards, review processes, escalation workflows, and operational governance frameworks
• Develop quality metrics, auditing methodologies, reviewer calibration programs, and random inspection systems
• Monitor quality risks across large-scale annotator and reviewer pipelines
• Identify and mitigate fraud, abuse, and quality risks, including multi-accounting, VPN/proxy usage, AI-generated responses, and low-quality contributors
• Investigate root causes behind quality issues such as declining acceptance rates, reviewer misalignment, annotation quality degradation, workflow inefficiencies, and client requirement mismatches
• Develop corrective actions and scalable solutions that improve project quality and customer acceptance rates
• Improve reviewer consistency, accountability, and operational traceability across projects
• Collaborate cross-functionally with Project Managers, Operations, Product, QA teams, and Leadership to drive quality initiatives
• Support the development of scalable systems and processes that improve quality outcomes without increasing operational overhead
• Contribute to 0→1 initiatives that strengthen quality management and operational excellence across the organization
Qualifications:
Required:
• Strong operational foundation in quality assurance, crowdsourcing operations, trust & safety, compliance operations, project operations, or related fields
• Experience identifying and solving operational problems through process design, governance frameworks, or quality systems
• Strong analytical thinking and root cause analysis capabilities
• Understanding of crowdsourcing challenges such as reviewer inconsistency, contributor quality management, fraud prevention, and operational scalability
• Ability to design scalable, traceable, and repeatable operational processes
• High ownership mindset with the ability to operate independently in ambiguous environments
• Strong written and verbal communication skills
• Excellent stakeholder management and cross-functional collaboration abilities
• Detail-oriented with a commitment to operational excellence
• Interest in AI, machine learning, and large-scale data operations
• Growth-oriented mindset with a bias toward continuous improvement and execution
Preferred:
• Experience working at AI data platforms, crowdsourcing platforms, trust & safety organizations, or large-scale annotation operations
• Experience managing reviewers, contributors, quality programs, or operational teams
• Familiarity with quality dashboards, QA tooling, workflow management systems, or operational reporting platforms
• Experience improving acceptance rates, quality metrics, or operational performance at scale
• Startup or high-growth environment experience
• Experience building quality systems and governance frameworks from 0→1
• Familiarity with AI, LLM, data annotation, or human-in-the-loop workflows
Company:
Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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