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Day Shift Ai Data Annotation Jobs (NOW HIRING)

Sunday

Data Annotation Lead

Redwood City, CA · On-site

Responsibilities : • Build a data annotation team • Manage the people side of data annotations ... Sunday is a robotics startup that builds home robots that utilize AI to assist with household tasks.

Sunday Inc

Data Annotation Lead

Redwood City, CA · On-site

This role is about building and leading a world class in-house data annotation team that is able to ... Ability to leverage AI to help improve productivity At Sunday Robotics, we're building technology ...

Bot Auto

Data Annotation Specialist

Houston, TX

High-quality data is the lifeblood of our "Physical AI" and the foundation of our autonomous ... Onsite Requirement: This position requires being onsite at our Houston, TX 5 days per week.

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Day Shift Ai Data Annotation information

How much do AI data annotators make?

AI data annotators typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Many positions are freelance or part-time, with some companies offering bonuses for accuracy and efficiency.

What are Day Shift AI Data Annotation jobs?

Day Shift AI Data Annotation jobs involve labeling and categorizing data, such as images, text, or audio, during daytime working hours to help train artificial intelligence systems. Annotators review raw data and apply tags or labels according to specific guidelines, ensuring AI models learn to recognize patterns accurately. These roles are essential for improving the quality and reliability of machine learning algorithms. Typically, the work is detail-oriented and may be performed in an office or remotely. Day shift positions appeal to those who prefer standard business hours.

Does data annotation really pay you?

Data annotation jobs, including day shift AI data annotation roles, typically pay hourly or per task rates, with earnings depending on the volume and complexity of annotations. Many companies offer remote work with flexible schedules, and payment is usually processed through direct deposit or online platforms. Earnings can vary based on experience, accuracy, and the employer's pay structure.

Which 3 jobs will survive AI?

Day Shift AI Data Annotation jobs are likely to persist because they require human judgment for complex or nuanced data labeling, which AI cannot fully replicate. Roles involving critical thinking, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled trades, are also expected to remain in demand despite AI advancements.

What are some common challenges faced by Day Shift AI Data Annotation specialists, and how can they be addressed?

Day Shift AI Data Annotation specialists often encounter challenges like maintaining focus and accuracy during repetitive tasks, meeting tight deadlines, and staying consistent with evolving annotation guidelines. To address these, it's helpful to take regular short breaks to reduce fatigue, actively participate in team discussions to clarify guidelines, and use quality assurance tools or peer reviews to ensure data accuracy. Many teams also offer support through continuous training and feedback, which can help annotators improve their skills and efficiency over time.

What is the difference between Day Shift Ai Data Annotation vs Data Labeler?

AspectDay Shift Ai Data AnnotationData Labeler
CredentialsHigh school diploma or equivalent; basic computer skillsHigh school diploma or equivalent; attention to detail
Work EnvironmentOffice or remote; computer-based tasksOffice or remote; computer-based tasks
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI trainingLabeling data for AI models

Both roles involve data annotation and labeling for AI systems, often in similar environments. The main difference lies in terminology; 'Day Shift Ai Data Annotation' emphasizes working during daytime hours, while 'Data Labeler' is a broader term used across various shifts and companies. Both positions require attention to detail and basic technical skills, making them closely related in the AI industry.

What are the key skills and qualifications needed to thrive as a Day Shift AI Data Annotation Specialist, and why are they important?

To thrive as a Day Shift AI Data Annotation Specialist, you need strong attention to detail, basic computer literacy, and proficiency in following instructions, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes simple scripting or spreadsheet software is typically required. Effective communication, time management, and the ability to stay focused during repetitive tasks are valuable soft skills in this role. These capabilities ensure high-quality, accurate data labeling, which is critical for training reliable AI models.

Is data annotation real or fake?

Data annotation is a real and essential process in AI development where human annotators label data such as images, text, or audio to train machine learning models. It involves accurate, manual work using tools and guidelines to ensure data quality, and it is a common job in AI data labeling roles.
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Day Shift Ai Data Annotation jobs near you

AI Data Annotation Specialist

BC Forward

Charlotte, NC

$56.08/hr

Other

Posted 24 days ago

Job description

Job Title: Application Programmer III Location: Charlotte, NC Duration: Contract - 12 months Pay Range: $56.08/hr (W2) Job ID: 373918 About BCforward BCforward is a leading global IT consulting and workforce solutions firm providing services and support to Fortune 500 and government clients. Founded in 1998, BCforward has grown with our customers needs into a full-service business solutions provider. With delivery centers and offices across North America and India, we take pride in building long-term relationships and delivering excellence through innovation, collaboration, and integrity.

Job Description We are seeking an AI Data Annotation Training Data Contractor to join our dynamic team. The ideal candidate will have strong experience in AI/ML data labeling, QA, and evaluation across NLP, information retrieval, entity extraction, routing/classification, semantic search, and RAG/LLM applications, and a proven ability to deliver accurate, consistent annotations and evaluation datasets at scale. Responsibilities: Annotate and label large datasets for AI/ML training and evaluation tasks across text, tabular, and retrieval workflows.

Create labels for query classification, intent detection, entity and time extraction, metric identification, semantic similarity, relevance ranking, and document retrieval quality. Tag and classify user queries, documents, entities, metadata, tool routing, structured vs. unstructured query type, human preference, and LLM response quality.

Perform QA reviews to ensure consistency, accuracy, completeness, and adherence to acceptance criteria; escalate ambiguities and edge cases. Participate in inter-annotator agreement, calibration sessions, and feedback loops to refine dataset quality. Assist in building evaluation datasets, benchmark suites, and golden sets for classifiers, retrieval systems, and LLM generation quality.

Review AI outputs, provide structured scoring and feedback, and help identify failure modes, hallucinations, routing issues, and retrieval gaps. Follow detailed annotation specifications and operational procedures; document decisions, edge cases, and standards. Support taxonomy and schema refinement; organize datasets, metadata, and labeling workflows across tools and platforms.

Work effectively within Agile development practices and collaborate with data science, ML engineering, and platform teams. Required Skills & Qualifications: Bachelor's degree or equivalent practical experience. Experience with data annotation, data labeling, QA, research operations, or analytical workflows.

Ability to follow complex technical instructions and detailed labeling guidelines with high accuracy. Strong attention to detail, organizational skills, and written communication. Comfort with large datasets, structured processes, spreadsheets, and labeling interfaces.

Ability to work independently, manage priorities, and meet deadlines in a fast-paced environment. Preferred Skills: Familiarity with RAG, LLM evaluation, semantic search, and information retrieval concepts. Experience working in Agile/Scrum settings and collaborating with ML engineering teams.

Why BCforward? At BCforward, we believe in advancing lives and careers. When you join our team, you gain access to: Competitive compensation and benefits.

Opportunities for growth with global clients. A supportive, inclusive culture that values innovation and people. Exposure to cutting-edge technologies and projects.

About Our Commitment BCforward is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, or veteran status.

Interested? Apply Now! If this sounds like the right opportunity for you, please apply with your most recent resume.

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