Data Annotator Jobs
Data Annotator jobs are open across technology, healthcare, autonomous vehicles, and e-commerce, from entry-level to senior and lead roles, with specializations in image labeling, natural language processing, and audio transcription. Scan the live roles below and apply to whichever ones fit.
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See related jobsAI Research Data Annotator (Part-Time)
Location: San Francisco, CA (On-site)
Type: Part-time contract, approximately 15–20 hours per week
Compensation: $20–$25/hour
About Engramme
Engramme is an AI research lab building Large Memory Models (LMMs) to give people perfect, infinite memory.
We're founded by Gabriel Kreiman, a former Harvard professor, and Spandan Madan, a former Harvard researcher. We're a small, ambitious team in San Francisco working on one of the most fundamental challenges in AI: memory.
About the Role
We're looking for part-time AI Research Evaluators / Data Annotators to help us train, evaluate, and improve our AI memory system.
Our technology connects information from everyday digital tools—such as email, messages, and documents—and surfaces relevant memories when they're useful.
You'll work directly with our research team on a variety of data labeling and evaluation tasks. These tasks are essential to developing and improving our Large Memory Models.
Examples of tasks include:
AI Memory Evaluation: Use Engramme's AI memory system during everyday digital activities and evaluate whether the memories it retrieves are relevant and useful.
Conversation Annotation: Review transcripts of recorded conversations and identify specific statements, facts, experiences, and other information that represent memories.
Memory Classification: Examine AI-generated memories and classify them based on criteria such as whether they contain spam, irrelevant information, or other undesirable content.
Research Data Annotation: Label and categorize different types of memory-related information to help researchers develop and train new algorithms.
Research Experiments: Participate in structured experiments, which may include recorded conversations with appropriate consent, and assist with other emerging data collection and labeling tasks.
Because we're developing a new category of AI, the specific tasks will evolve over time. You'll receive instructions and training for each task and work closely with our researchers to refine how memory data is evaluated and annotated.
This is a hands-on data evaluation and annotation role, not a software engineering or AI research scientist position.
What You'll Do
Complete a variety of data annotation and evaluation tasks during scheduled in-office sessions.
Review text, conversation transcripts, and AI-generated memories to produce accurate, consistent labels.
Use Engramme's memory system during everyday digital activities and provide feedback on the relevance and usefulness of retrieved memories.
Apply task-specific annotation guidelines and exercise judgment when encountering ambiguous information.
Complete a high volume of careful, consistent annotations while maintaining quality standards.
Identify unclear instructions, edge cases, and recurring issues, and share observations with our research team.
Adapt to new annotation tasks as our research priorities and experiments evolve.
What We're Looking For
Strong attention to detail: You can carefully examine information and make consistent, well-reasoned judgments.
Reliability and consistency: You're comfortable performing repetitive tasks accurately over extended periods.
Good judgment: You can interpret nuanced instructions and recognize when an example is ambiguous or requires clarification.
Adaptability: You're comfortable learning new tasks and working with guidelines that may evolve as our research progresses.
Digital literacy: You're comfortable using web browsers, email, messaging applications, and other everyday digital tools.
Clear communication: You can explain your reasoning, flag unclear cases, and provide constructive feedback.
Availability: You can work approximately 15–20 hours per week in person at our San Francisco office.
Must be at least 18 years old.
Some evaluation tasks may involve connecting your own digital information to our system, subject to informed consent and appropriate privacy protections.
No prior AI, programming, or research experience is required. College students and recent graduates are welcome to apply.
Why Join Us?
Contribute directly to cutting-edge AI research and help develop a new category of AI technology.
Work alongside researchers and engineers developing Large Memory Models.
Gain firsthand exposure to how AI systems are trained, evaluated, and improved.
Help shape annotation guidelines and evaluation methods for an emerging area of AI research.
Enjoy flexible part-time scheduling and a collaborative in-office environment.
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Find Data Annotator JobsData Annotator Job Market
Who's Hiring
What Employers Look For
The qualifications that appear most often in data annotator jobs.
- High attention to detail and consistent accuracy across large batches of data
- Familiarity with annotation platforms such as Labelbox, Scale AI, or similar tools
- Ability to follow detailed annotation guidelines and style guides precisely
- Experience with image, text, audio, or video labeling depending on the team's focus
- Strong written English for text-based and natural language processing annotation tasks
- High school diploma or equivalent, with a bachelor's degree preferred for senior roles
Tips for Your Data Annotator Job Search
Tailor your resume to annotation types
Hiring managers scan for the specific annotation modalities you've worked with, such as bounding boxes, semantic segmentation, or sentiment tagging. List each type explicitly rather than writing 'data annotation experience,' which tells them nothing about your actual skill set.
Highlight your quality assurance work
Many candidates omit QA experience, but employers weight it heavily. If you've reviewed or audited another annotator's output, flagged edge cases, or maintained inter-annotator agreement scores, call that out directly on your resume with concrete examples.
Apply early to roles that fit
Migrate Mate lists data annotator openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter by domain when targeting openings
Data annotation is not one job. Medical imaging annotation requires different skills than autonomous vehicle lidar labeling. Narrow your search by industry domain so your application speaks directly to the employer's training data pipeline and you're not competing as a generalist.
Prepare a sample annotation task for interviews
Many employers give a paid or unpaid trial task before extending an offer. Practice annotating under a time constraint, document your decision-making on ambiguous cases, and be ready to explain your edge-case logic out loud during a live annotation exercise.
Negotiate rate on project-based contracts
A significant share of data annotator work is contract or gig-based. Before accepting, ask whether the rate is per hour or per task, what the expected throughput is, and whether accuracy bonuses exist. These details change your effective rate more than the headline figure.
Data Annotator Jobs: Frequently Asked Questions
Which companies are hiring the most data annotators?
The companies hiring the most data annotators right now include Enabled Intelligence, Aisera, and Engramme, with the largest share of openings in California, Idaho, and Missouri, based on current listings on Migrate Mate as of November 2026. Demand is especially strong at companies building large language models and computer vision systems.
How many data annotator jobs are remote?
About 100% of data annotator openings are fully remote or hybrid as of November 2026, making it one of the more remote-accessible roles in the technology sector. Text and audio annotation tasks tend to have the highest remote share, while hardware-dependent work such as lidar point cloud labeling is more likely to require an on-site setup.
How do you become a data annotator?
Start by learning the fundamentals of machine learning data pipelines so you understand why annotation quality matters. Practice with free or open-source annotation tools to build hands-on experience with common task types like bounding boxes, named entity recognition, and transcription. Apply to entry-level or contract roles that offer structured guidelines, complete any trial tasks carefully, and document your accuracy and throughput as you go.
Can you get hired as a data annotator with no experience?
Yes, many data annotator roles are explicitly open to candidates without prior experience, particularly contract or project-based positions where employers provide their own annotation guidelines and tools. The strongest way to stand out without a formal background is to demonstrate attention to detail, complete any employer-assigned trial tasks with high accuracy, and show familiarity with at least one common annotation platform through self-directed practice.
What does the data annotator interview process look like?
Most data annotator interviews involve a short screening call followed by a practical annotation task, which may be timed and graded for accuracy. Some employers also ask you to walk through your decision-making on ambiguous edge cases, testing whether you can apply guidelines consistently rather than guess. For senior or lead roles, expect questions about quality control processes, managing annotation pipelines, and experience training or reviewing other annotators.
Where can I find and apply to data annotator jobs?
You can find and apply to data annotator jobs on Migrate Mate, which lists current openings from across the United States in one place. Search for roles that match your annotation specialization and experience level, then apply directly to each listing that fits.
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