ZipRecruiter

Log In

2

Internship Remote Data Annotation Jobs in Irvine, CA

Novoco

2027 Staff Accountant Intern

Long Beach, CA · On-site +1

$29/hr

Our trendsetting interns learn hands-on what it takes to be a staff accountant for a growing ... Some positions at Novogradac may be open to remote or hybrid work arrangements depending on ...

Zorts Sports

Software Engineer

Claremont, CA · Remote

$100K - $125K/yr

Software Engineer Flash Biometrics | Hybrid/Remote - Claremont, CA Salary Range: $100,000-$125,000 ... What We're Looking For Core Qualifications * 1+ year of professional (non-internship) software ...

Directive

Controller (Remote US)

Irvine, CA · Remote

$125K - $140K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... interns, helping develop technical skills, ensuring accuracy in their work, and fostering ... data, identify discrepancies, and make informed predictions What We Offer: * We have a set living ...

BPM LLP

Tax Intern (January 2027)

Irvine, CA · On-site +1

$29 - $33/hr

You will apply critical analysis to data, preparing meticulous work papers and tax returns in ... Champion sustainable workplace practices by supporting remote-first operations, promoting paperless ...

BPM LLP

Tax Intern (January 2027)

Long Beach, CA · On-site +1

$29 - $33/hr

You will apply critical analysis to data, preparing meticulous work papers and tax returns in ... Champion sustainable workplace practices by supporting remote-first operations, promoting paperless ...

All JobsData Annotation JobsInternship Remote Data Annotation JobsInternship Remote Data Annotation Jobs in Irvine, CA

Internship Remote Data Annotation information

See Irvine, CA salary details

$12

$24

$45

How much do internship remote data annotation jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for internship remote data annotation in Irvine, CA is $24.16, according to ZipRecruiter salary data. Most workers in this role earn between $18.56 and $26.30 per hour, depending on experience, location, and employer.

What is a remote data annotation internship?

A remote data annotation internship is a temporary position where interns work from home or another remote location to label, categorize, or tag data such as images, text, or audio. This annotated data is often used to train machine learning models and improve artificial intelligence systems. Interns typically use specialized platforms or tools to complete their tasks, and gain hands-on experience in data handling, quality control, and understanding AI workflows. The internship is ideal for those interested in technology, data science, or AI, and often requires strong attention to detail and good communication skills.

What does a remote data annotation intern do, and how is performance evaluated?

As a remote data annotation intern, your primary tasks will involve reviewing and labeling data—such as images, text, or audio—according to specific guidelines provided by your team. You'll likely work with annotation tools, follow detailed instructions to ensure high-quality and consistent labeling, and may participate in quality assurance checks. Performance is generally evaluated based on annotation accuracy, speed, and your ability to follow instructions, with regular feedback provided via virtual meetings or project management platforms. Effective communication and attention to detail are key to succeeding in this collaborative, remote environment.

What skills and qualifications are needed to thrive as a remote data annotation intern?

To thrive as a Remote Data Annotation Intern, you need attention to detail, basic data processing skills, and familiarity with labeling guidelines, generally supported by a high school diploma or relevant coursework. Experience with annotation platforms, spreadsheets, and sometimes basic programming tools or machine learning frameworks is often required. Strong communication, time management, and the ability to follow precise instructions are valuable soft skills in this role. These skills ensure high-quality, accurate data labeling, which is critical for training reliable machine learning models.

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

AspectInternship Remote Data AnnotationData Labeling Specialist
CredentialsTypically students or entry-level with basic computer skillsRelevant experience or certifications in data annotation or related fields
Work EnvironmentRemote, flexible hours, often part-timeRemote or on-site, depending on employer, often full-time
Industry UsageCommon in AI/ML projects, tech companies, research institutionsUsed in AI/ML, autonomous vehicles, healthcare, and tech sectors

Internship Remote Data Annotation roles are usually entry-level, temporary positions aimed at gaining experience, while Data Labeling Specialists are more experienced roles focused on accurately annotating data for machine learning models. Both roles are essential in AI development but differ in experience requirements and job scope.

What are popular job titles related to Internship Remote Data Annotation jobs in Irvine, CA?

For Internship Remote Data Annotation jobs in Irvine, CA, the most frequently searched job titles are:

What job categories do people searching Internship Remote Data Annotation jobs in Irvine, CA look for?

The top searched job categories for Internship Remote Data Annotation jobs in Irvine, CA are:

What cities near Irvine, CA are hiring for Internship Remote Data Annotation jobs?

Cities near Irvine, CA with the most Internship Remote Data Annotation job openings:

Internship Remote Data Annotation jobs near you
Infographic showing various Internship Remote Data Annotation job openings in Irvine, CA as of August 2026, with employment types broken down into 36% Full Time, 43% Part Time, and 21% Contract. Highlights an 100% Remote job distribution, with an average salary of $50,245 per year, or $24.2 per hour.

Science and Conservation Associate

Laguna Ocean Foundation

Laguna Beach, CA • Remote

Part-time

Re-posted 11 days ago

Job description

Science and Conservation Associate

Location: Laguna Beach, California
Organization: Laguna Ocean Foundation (LOF)
Status: Part-time (Contingent upon funding)

About Laguna Ocean Foundation

The Laguna Ocean Foundation (LOF) is a nonprofit dedicated to protecting and restoring Laguna Beach’s coastal ecosystems through science, education, conservation, and community engagement. Working with universities, government agencies, and community partners, LOF is advancing innovative coastal monitoring by integrating field ecology with drones, GIS, machine learning, environmental DNA (eDNA), and citizen science.

Position Summary

LOF seeks a motivated Science and Conservation Associate to support the technical foundation of its research and conservation programs. This position transforms field observations into high-quality geospatial data that inform ecological monitoring, restoration, conservation planning, and public education across Laguna Beach’s Marine Protected Areas.

The Associate will manage geospatial workflows—from drone and underwater ROV surveys through machine-learning analysis, GIS integration, habitat mapping, and scientific visualization. Working closely with LOF biologists, university researchers, GIS specialists, and interns, the Associate will help build one of California’s most comprehensive long-term coastal spatial datasets while supporting scientific publications, grant-funded research, StoryMaps, and emerging conservation technologies.

Position contingent upon funding.

Primary Responsibilities

Remote Sensing & Field Operations

● Conduct drone surveys under FAA Part 107 regulations.

● Operate underwater ROVs to document marine habitats.

● Coordinate field logistics, equipment, permits, and data collection.

GIS & Spatial Analysis

● Process drone imagery into ortho-mosaics and elevation models.

● Maintain GIS databases and produce habitat, species, and change-detection maps.

● Integrate remote sensing with biological field data.

Artificial Intelligence & Data Science

● Develop and apply machine-learning models for habitat and species classification.

● Improve classification workflows and support Python-based geospatial automation.

Research & Communication

● Collaborate with scientists and partner institutions on applied conservation research.

● Assist with scientific publications, technical reports, grant proposals, and conference presentations.

● Develop Story-Maps, dashboards, and visual products that communicate science to diverse audiences.

Training & Program Support

● Mentor interns and volunteers in GIS, drone operations, and data collection.

● Help establish standardized monitoring and data management protocols.

Required Qualifications

● Bachelor’s degree in GIS, Geography, Environmental Science, Marine Science, Engineering, Computer Science, Data Science, Ecology, or related field (or equivalent experience).

● At least one year of experience in remote sensing, GIS, spatial analysis, or environmental monitoring.

● Proficiency with ArcGIS Pro and/or QGIS.

● Experience with Python for geospatial analysis or machine learning.

● Experience processing drone imagery using photogrammetry.

● Familiarity with image classification or computer vision workflows.

● Knowledge of geospatial data management and reproducible workflows.

● FAA Part 107 certification (or ability to obtain).

● Excellent organizational, communication, and technical writing skills.

Preferred Qualifications

● Experience in coastal or marine ecosystems.

● Underwater ROV experience.

● Familiarity with eDNA, ecological monitoring, or conservation research.

● Experience working with universities, government agencies, or nonprofits.

● Grant writing or peer-reviewed publication experience.

● Experience with ArcGIS Online, StoryMaps, Dashboards, or web GIS.

● Knowledge of TensorFlow, PyTorch, Detectron2, or similar frameworks.

● Experience with cloud-based geospatial workflows.

Please send your resume and cover letter to: Please send in your material by July 31 to info@lagunaoceanfoundation.org

E04JI800ui4140992ms