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Data Annotation Tech Remote Jobs in Pennsylvania

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Data Annotation Tech Remote information

What is the difference between Data Annotation Tech Remote vs Data Labeling Specialist?

AspectData Annotation Tech RemoteData Labeling Specialist
CredentialsBasic technical skills, sometimes certifications in data annotation toolsSimilar credentials, often with experience in labeling software
Work EnvironmentRemote, often freelance or contract-basedRemote or on-site, depending on employer
Industry UsageUsed across AI, machine learning, and data science companiesCommon in AI, autonomous vehicles, and tech firms

Both roles involve labeling data for machine learning models, with similar credentials and remote work options. The main difference lies in job titles used by employers, but their responsibilities and industry applications overlap significantly.

What are Data Annotation Tech Remote jobs?

Data Annotation Tech Remote jobs involve working from home or another remote location to label, tag, or classify data such as text, images, audio, or video. This work is essential for training and improving artificial intelligence and machine learning models. Data annotators use specialized software tools to accurately identify and categorize data according to specific guidelines provided by employers. These roles require attention to detail, consistency, and sometimes subject-matter expertise, depending on the project. Remote data annotation jobs are popular because they often offer flexible schedules and the ability to work from anywhere.

What are some common challenges faced by remote Data Annotation Technicians, and how can they be addressed?

Remote Data Annotation Technicians often encounter challenges such as maintaining consistent annotation quality, managing repetitive tasks, and ensuring clear communication with team leads or project managers. To address these, it's helpful to establish a structured daily routine, use collaboration tools to stay connected with the team, and regularly review project guidelines to ensure accuracy. Many organizations also provide feedback loops and quality assurance checks, so being proactive in seeking feedback can help improve performance and job satisfaction.

What are the key skills and qualifications needed to thrive as a Data Annotation Tech (Remote), and why are they important?

To excel as a Data Annotation Tech (Remote), you need attention to detail, basic computer literacy, and familiarity with data labeling practices, often supported by a high school diploma or equivalent. Proficiency with annotation tools such as Labelbox, Supervisely, or proprietary platforms is typically required, and training in data privacy or quality assurance may be beneficial. Strong communication, time management, and the ability to focus independently are standout soft skills for this remote role. These competencies are crucial to ensure accurate, high-quality data labeling that directly impacts the effectiveness of AI and machine learning models.
What are the most commonly searched types of Data Annotation Tech jobs in Pennsylvania? The most popular types of Data Annotation Tech jobs in Pennsylvania are:
What are popular job titles related to Data Annotation Tech Remote jobs in Pennsylvania? For Data Annotation Tech Remote jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Data Annotation Tech Remote jobs in Pennsylvania look for? The top searched job categories for Data Annotation Tech Remote jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Data Annotation Tech Remote jobs? Cities in Pennsylvania with the most Data Annotation Tech Remote job openings:
Infographic showing various Data Annotation Tech Remote job openings in Pennsylvania as of June 2026, with employment types broken down into 41% Full Time, 32% Part Time, and 27% Contract. Highlights an 100% Remote job distribution.
Remote AI Analyst

Remote AI Analyst

Turing

Reading, PA • Remote

Other

Posted 4 days ago


Job description

Calling US-based Google Wallet Users for an Exclusive AI Evaluation Project. Open to recent graduates, professionals, and anyone interested in AI. If you actively use Google Wallet, you may already qualify for this exciting paid opportunity. No specialized technical background is required—just your everyday experience, attention to detail, and willingness to work with advanced AI tools. Earn while helping shape the future of AI.


About Turing:

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality data, evaluations, and reinforcement learning environments that improve model capabilities in coding, reasoning, tool use, and multimodality.


Role Overview :

Turing is seeking detail-oriented AI Analysts based in the United States to support a Google Wallet evaluation project. This is a Generalist role and do not require candidates from any specific background. In this role, you will interact with Gemini models, execute evaluation workflows, review model responses, and document findings using Google Sheets.


Duration of Contract - 10 weeks

Requirement - Full time Contract - 40hrs/week - 4 hours PST Overlap


What You'll Do Day-to-Day :

- Evaluate Gemini model responses by submitting prompts and assessing output quality, accuracy, and relevance.

- Review and document evaluation results, ratings, and observations using Google Sheets.

- Participate in Google Wallet-related testing scenarios and provide structured feedback on user experiences.

- Follow project guidelines and quality standards to support AI model improvement and evaluation objectives.


Requirements :

- Must be based in the United States and actively use Google Wallet.

- Must have at least one linked payment method (credit card, debit card, or bank account) and a minimum of 5 passes stored in Google Wallet.

- Must have a Plaid account or be willing to connect a bank account through Plaid for project participation.

- Must be comfortable using Google Wallet, linked payment methods, and Plaid connectivity as part of evaluation activities.

- Strong attention to detail, analytical thinking, and ability to follow structured guidelines.

- Experience with Google Sheets and familiarity with Gemini or other generative AI tools is preferred.

- Prior experience in AI evaluation, data annotation, content review, quality assurance, or fintech-related projects is a plus.