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Temporary Ai Data Annotation Jobs (NOW HIRING)

Artech

Annotation Operations Lead

Sunnyvale, CA · On-site

Experience with data labeling platforms. * Strong understanding of AI/ML concepts, annotation workflows, and data quality assurance. * Basic knowledge of SQL and/or Python for data querying and ...

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

... building data annotation and dataset management tools. Company : Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is ...

Encord

Human Data Operations Strategist

San Francisco, CA · On-site

$150K - $230K/yr

Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams * Ensure the highest standards of data ...

Mercor

Network Engineer - Data Annotation

New York, NY · Remote

$50 - $70/hr

About the job Mercor connects elite creative and technical talent with leading AI research labs ... Position: Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation ...

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Temporary Ai Data Annotation information

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

To thrive as a Temporary AI Data Annotation Specialist, you need keen attention to detail, strong analytical skills, and the ability to follow complex guidelines, often supported by a high school diploma or equivalent. Familiarity with data labeling platforms, annotation tools like Labelbox or Prodigy, and basic computer literacy are typically required. Reliability, consistency, and the ability to work independently stand out as valuable soft skills in this role. These competencies are essential for producing high-quality, accurate data that directly impacts the effectiveness of machine learning models.

What are some common challenges faced in a Temporary AI Data Annotation role, and how can they be managed?

One of the main challenges in a Temporary AI Data Annotation position is maintaining consistent accuracy and attention to detail, especially when working with large volumes of data. Annotation guidelines can be complex and may change depending on project requirements, so adaptability and clear communication with the team are key. Managing repetitive tasks while ensuring high-quality work can be demanding, but using productivity tools and taking regular breaks can help maintain focus. Collaborating with quality assurance leads and participating in feedback sessions are also important for continuous improvement.

What are Temporary AI Data Annotation jobs?

Temporary AI Data Annotation jobs involve labeling, categorizing, or tagging data such as images, text, audio, or video for the purpose of training artificial intelligence (AI) and machine learning models. These roles are often short-term or contract positions, as they are needed for specific projects or during certain stages of data processing. Annotators play a critical role in ensuring the quality and accuracy of datasets, which directly impacts the performance of AI systems. No advanced technical skills are usually required, but attention to detail and consistency are important. These jobs may be offered remotely or on-site, depending on the employer.

What is the difference between Temporary Ai Data Annotation vs Data Labeler?

AspectTemporary Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic skills, sometimes specific software knowledge
Work EnvironmentRemote or on-site, project-basedRemote or on-site, often similar settings
Industry UsageAI, machine learning, tech companiesAI, autonomous vehicles, tech sectors
Job FocusAnnotating data for AI trainingLabeling data for machine learning models

Temporary Ai Data Annotation involves short-term projects focused on preparing data for AI systems, while Data Labeler is a broader role that includes labeling various data types for machine learning. Both roles require similar skills and are used in tech industries, but Temporary Ai Data Annotation emphasizes project-based work specifically for AI training datasets.

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Temporary Ai Data Annotation jobs near you
Infographic showing various Temporary Ai Data Annotation job openings in the United States as of May 2026, with employment types broken down into 66% Full Time, 31% Part Time, and 3% Contract. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.
Data Scientist - Survey Design, Data Annotation, and Machine Learning Evaluation

Data Scientist - Survey Design, Data Annotation, and Machine Learning Evaluation

Apple

Cupertino, CA • On-site

Full-time

Posted 14 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Apple is where individual imaginations gather together, committing to the values that lead to.great work. Every new product we build, service we create, or experience we deliver is the.result of us making each other's ideas stronger. The diversity of our people and their thinking.inspires the innovation that runs through everything we do. When we bring everybody in, we.can do the best work of our lives. Here, you'll do more than join something - you'll add.something.
The Special Projects team at Apple is developing novel user-facing conversational features thatleverage the multimodal capabilities of state-of-the-art foundation models. As part of thisprocess, we generate real-world and simulated data, gather human data annotations, analyzethe results, and use them to build and evaluate Large Language Model judges. We are lookingfor a skilled Data Scientist to join our Machine Learning Evaluations teams. This person willwork closely with ML Engineers to manage and analyze our human and automated dataannotation processes, and to develop, test, and refine LLM judges for generative AI modelevaluation. A successful candidate is experienced in survey design, data annotation, LLMprompt engineering and prompt optimization, and has strong statistical analysis skills.
BA or Master's degree in Data Science, Statistics, or a quantitative social science field 2+ years of hands-on experience working in survey design and human data annotation Proficiency in Python Excellent communication skills
PhD in Data Science, Statistics, or a quantitative social science field Hands-on industry experience with product-focused statistical analysis Experience working with large-scale multimodal data and data-annotation pipelines Experience with LLM prompt engineering & prompt optimization Experience with LLM auto-judges for generative AI model evaluation A track record of publications or technical presentations in Data Science or a related field Excellent at cross-functional collaboration

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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