Data Annotation Specialist Jobs
Data Annotation Specialist jobs are open across technology, artificial intelligence, healthcare, and e-commerce, from entry-level reviewer to senior specialist and team lead, with specializations in NLP labeling, image and video annotation, and quality assurance. See the openings below and apply to the ones that match your experience.
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The Gatos team is a cross-discipline production and research data annotation team within Video Engineering. We deliver the labeled data that algorithm teams use to build and evaluate features for millions of devices and users around the world. We're looking for an operations engineer who will own annotation projects from the first request to delivered data. That includes a growing set of AI-assisted and agent-based workflows, where models do much of the labeling and people guide, review and verify the results.
Description
In this role you will: Work with algorithm teams to define what success looks like for each data request, and translate it into a clear task specification, labeling guidelines and a quality bar. Own annotation projects end to end, from pilot through production to delivery, and be accountable for quality, schedule and the usefulness of the data to the customer. Design, implement, and run AI-assisted labeling workflows: models pre-label, automated steps handle routine work, and people focus on judgment and verification. Measure the impact on quality, throughput and cost. Set acceptance criteria, run quality checks and audits, analyze error patterns, and close the loop with guideline owners and labeling teams. Track project status and risks, and raise issues early with a clear ask and a root cause. Collaborate with data scientists, software and infrastructure engineers, data collection teams, business operations, and external labeling teams across time zones. Improve how the team works by building and sharing tools and workflows that use AI.
Minimum Qualifications
- BS and a minimum of 3 years relevant industry experience.
- Experience delivering data for machine learning, such as data annotation, data operations, or technical program management.
- Experience defining annotation tasks, labeling guidelines, and quality metrics.
- Experience serving as the accountable owner for projects that span multiple teams, from ambiguous requirements to delivery.
- Experience working hands-on with data and technical tools, such as writing basic SQL queries or scripts, analyzing data in spreadsheets, or configuring labeling platforms.
Preferred Qualifications
- Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including judging where people must stay in the loop.
- Experience with Python or SQL for data analysis and automation.
- Familiarity with computer vision data such as images, video, depth, or 3D.
- Experience building reports or dashboards for cross-functional partners.
- Clear written and verbal communication, including status updates and escalations to senior leaders.
- Works effectively across time zones with internal teams and external partners.
- Defines success with customers before starting the work, and adapts as their needs change.
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
The Gatos team is a cross-discipline production and research data annotation team within Video Engineering. We deliver the labeled data that algorithm teams use to build and evaluate features for millions of devices and users around the world. We're looking for an operations engineer who will own annotation projects from the first request to delivered data. That includes a growing set of AI-assisted and agent-based workflows, where models do much of the labeling and people guide, review and verify the results.
Description
In this role you will: Work with algorithm teams to define what success looks like for each data request, and translate it into a clear task specification, labeling guidelines and a quality bar. Own annotation projects end to end, from pilot through production to delivery, and be accountable for quality, schedule and the usefulness of the data to the customer. Design, implement, and run AI-assisted labeling workflows: models pre-label, automated steps handle routine work, and people focus on judgment and verification. Measure the impact on quality, throughput and cost. Set acceptance criteria, run quality checks and audits, analyze error patterns, and close the loop with guideline owners and labeling teams. Track project status and risks, and raise issues early with a clear ask and a root cause. Collaborate with data scientists, software and infrastructure engineers, data collection teams, business operations, and external labeling teams across time zones. Improve how the team works by building and sharing tools and workflows that use AI.
Minimum Qualifications
- BS and a minimum of 3 years relevant industry experience.
- Experience delivering data for machine learning, such as data annotation, data operations, or technical program management.
- Experience defining annotation tasks, labeling guidelines, and quality metrics.
- Experience serving as the accountable owner for projects that span multiple teams, from ambiguous requirements to delivery.
- Experience working hands-on with data and technical tools, such as writing basic SQL queries or scripts, analyzing data in spreadsheets, or configuring labeling platforms.
Preferred Qualifications
- Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including judging where people must stay in the loop.
- Experience with Python or SQL for data analysis and automation.
- Familiarity with computer vision data such as images, video, depth, or 3D.
- Experience building reports or dashboards for cross-functional partners.
- Clear written and verbal communication, including status updates and escalations to senior leaders.
- Works effectively across time zones with internal teams and external partners.
- Defines success with customers before starting the work, and adapts as their needs change.
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
See All 11 Data Annotation Specialist Jobs
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Find JobsData Annotation Specialist Job Market
Who's Hiring
What Employers Look For
The qualifications that appear most often in data annotation specialist jobs.
- Experience with annotation platforms such as Label Studio, Scale AI, or similar tooling
- Strong attention to detail and ability to follow complex labeling guidelines consistently
- Familiarity with NLP, computer vision, or audio labeling tasks depending on the domain
- Bachelor's degree in linguistics, computer science, or a related field, or equivalent experience
- Ability to meet daily throughput targets while maintaining high inter-annotator agreement scores
- Experience with quality assurance workflows, including reviewing and auditing labeled datasets
Tips for Your Data Annotation Specialist Job Search
Tailor your resume for annotation tools
List every annotation platform you've used by name, including any domain-specific tooling for text, image, or audio tasks. Hiring managers scan for tool familiarity first, so burying it under general skills costs you interviews.
Highlight domain expertise on your resume
Employers in medical imaging, autonomous vehicles, or legal NLP pay a premium for annotators who already understand the subject matter. Call out your domain knowledge explicitly rather than letting reviewers infer it from job titles.
Apply early to roles that fit
Migrate Mate lists data annotation specialist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by annotation modality
Text, image, video, audio, and 3D point-cloud annotation require different skill sets. When you search for roles, use modality keywords so you're not wasting applications on work your background doesn't support.
Prepare a sample annotation artifact
Many interviews include a live labeling task or ask you to walk through how you resolved an ambiguous edge case. Having a documented example, even from a freelance or personal project, shows you can explain your reasoning under real conditions.
Negotiate quality benchmarks, not just pay
Data annotation roles are often evaluated against inter-annotator agreement scores or defect rates. Ask during the offer stage what the team's current benchmarks are so you know exactly what performance standard you're being hired against.
Data Annotation Specialist Jobs: Frequently Asked Questions
Which companies are hiring the most data annotation specialists?
Caterpillar, Genesis, and Correlation One are hiring the most data annotation specialists right now, with openings concentrated in California, Texas, and Minnesota, based on current listings on Migrate Mate as of November 2026. Demand is highest at AI product companies and technology firms building or expanding training datasets for machine learning models.
How many data annotation specialist jobs are remote?
About 75% of data annotation specialist openings are fully remote or hybrid as of November 2026, reflecting the digital nature of labeling work. Text and NLP annotation roles tend to be the most remote-friendly, while positions involving specialized hardware, 3D sensors, or on-site data collection are more likely to require a physical location.
How do you become a data annotation specialist?
Start by learning the fundamentals of machine learning data pipelines so you understand why accurate labels matter. Practice on open annotation tools and work through publicly available labeling tasks to build hands-on experience. Develop expertise in at least one domain, such as medical imaging or natural language processing, and document your work with concrete examples you can show during interviews.
Can you get a data annotation specialist job with little or no experience?
Yes, many entry-level data annotation roles require no prior professional experience, only the ability to follow detailed guidelines and produce consistent, accurate labels. Completing freelance annotation tasks on crowdsourced platforms, contributing to open-source labeling projects, or building a small self-directed dataset can demonstrate practical ability when your resume is otherwise thin.
What does the data annotation specialist interview process look like?
Most processes start with a screening call focused on your familiarity with annotation tools and the relevant data domain. A practical labeling assessment follows, where you label a sample set and explain your decisions on ambiguous cases. Final rounds typically involve a discussion of quality metrics, your approach to resolving guideline conflicts, and how you maintain accuracy under production throughput expectations.
Where can I find and apply to data annotation specialist jobs?
You can find and apply to data annotation specialist jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your annotation specialty and experience level, then apply directly to each one that fits.
See All 11 Data Annotation Specialist Jobs
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