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Entry Level Ai Data Rater Jobs (NOW HIRING)

Abaka AI

Quality Project Associate

Abaka AI is a trusted data partner for AI companies, focusing on providing scalable data solutions ... rates, reviewer misalignment, annotation quality degradation, workflow inefficiencies, and client ...

Welo Data

English AI Search Evaluator

Raymondville, TX · Remote

In this role, you'll use your keen understanding of human intent to provide subjective and objective ratings based on project guidelines. Your feedback will directly help develop and augment AI data ...

ABB

AI & Data Scientist Intern - Fall 2026

Cary, NC · Hybrid

$20 - $34/hr

Conduct data analysis and preprocessing to back AI projects. * Contribute to the creation of AI ... The hourly rate is determined by things such as the successful applicant's qualifications ...

ABB

AI & Data Scientist Intern - Fall 2026

Cary, NC · On-site

$20 - $34/hr

Conduct data analysis and preprocessing to back AI projects. * Contribute to the creation of AI ... The hourly rate is determined by things such as the successful applicant's qualifications ...

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Entry Level Ai Data Rater information

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How much do entry level ai data rater jobs pay per hour?

As of Jun 25, 2026, the average hourly pay for entry level ai data rater in the United States is $19.05, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $21.39 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level AI Data Rater, and why are they important?

To thrive as an Entry Level AI Data Rater, you need strong analytical skills, attention to detail, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with web browsers, online research tools, and proprietary rating platforms is typically required; some roles may also ask for knowledge of specific guidelines or quality assurance systems. Excellent communication, critical thinking, and time management are crucial soft skills for interpreting data and meeting productivity targets. These competencies ensure accurate data labeling and evaluation, directly impacting the quality of AI training and system performance.

What is the difference between Entry Level Ai Data Rater vs Data Labeler?

AspectEntry Level Ai Data RaterData Labeler
Required CredentialsHigh school diploma or equivalent; no specialized certification often neededHigh school diploma or equivalent; may require basic training
Work EnvironmentRemote or office-based; working with AI datasetsRemote or on-site; labeling data for machine learning models
Employer & Industry UsageTech companies, AI startups, data annotation firmsTech companies, data annotation services, AI development firms

Both roles involve working with data to improve AI systems, but Entry Level Ai Data Raters often focus on evaluating AI outputs, while Data Labelers primarily annotate data. The roles are similar in credentials and work environment, with slight differences in job focus.

What are the most common challenges faced by Entry Level AI Data Raters, and how can they be addressed?

One of the most common challenges for Entry Level AI Data Raters is maintaining consistency and accuracy when evaluating large volumes of data. Since the work often involves repetitive tasks and adhering to detailed guidelines, it can be easy to overlook small errors or become fatigued. To overcome this, it's helpful to take regular breaks, stay organized, and continually review the provided instructions. Collaborating with team leads or peers to clarify uncertainties can also improve performance and ensure high-quality results.

What is an Entry Level AI Data Rater?

An Entry Level AI Data Rater is a professional who evaluates, labels, and rates data—such as text, images, or search results—to help improve artificial intelligence systems. Their work ensures that AI models can better understand and process human language and behavior. This role typically involves following specific guidelines to assess the accuracy and relevance of data, providing valuable feedback that helps train machine learning algorithms. Entry level positions usually require attention to detail, basic computer skills, and the ability to follow instructions closely.
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Entry Level Ai Data Rater jobs near you
Infographic showing various Entry Level Ai Data Rater job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, and 4% Part Time. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $39,629 per year, or $19.1 per hour.

Quality Project Associate

Abaka AI

Remote

Full-time

Posted 23 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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