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Internship Remote Data Annotation Jobs in Alexandria, VA

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

This is a Remote (work from home) position. Most of the machine learning work that reaches ... Review the ground-truth work our annotation teams produce, and feed problems back into the ...

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

This is a Remote (work from home) position. Most of the machine learning work that reaches ... Review the ground-truth work our annotation teams produce, and feed problems back into the ...

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

This is a Remote (work from home) position. Most of the machine learning work that reaches ... Review the ground-truth work our annotation teams produce, and feed problems back into the ...

GeoYeti

Senior SAR Imagery Scientist

Springfield, VA · Remote

$190K - $235K/yr

This role blends remote sensing science with applied data engineering and AI. What You'll Do ... Support annotation workflows by identifying and flagging imagery containing objects of interest

Saalex

General Consideration

Washington, DC · On-site +1

... Data Analytics and Business Intelligence Services and Information Technology Services. Position Types: Full-Time/Part-Time/Internship Work Location: Various positions available, hybrid remote/in ...

ihs

Digital Marketing Intern (Remote)

Arlington, VA · Remote

$20/hr

Pulls and organizes platform data to prepare regular performance reports for the Digital Content ... This internship will start on September 21, 2026, and end on December 11, 2026 (start and end dates ...

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Internship Remote Data Annotation information

See Alexandria, VA salary details

$12

$24

$45

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

As of Aug 27, 2026, the average hourly pay for internship remote data annotation in Alexandria, VA is $24.09, according to ZipRecruiter salary data. Most workers in this role earn between $18.51 and $26.25 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 Alexandria, VA?

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

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

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What cities near Alexandria, VA are hiring for Internship Remote Data Annotation jobs?

Cities near Alexandria, VA with the most Internship Remote Data Annotation job openings:

Internship Remote Data Annotation jobs near you

Machine Learning Engineer

Theory AI

Washington, DC • Remote

$3.0K/wk

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 5 days ago

Job description

Benefits
  • Fully paid medical, dental and vision
  • 401(k) with employer contribution
  • Unlimited paid time off with a four-week minimum
  • $3,000 a year for conferences, courses and certifications
  • Home office budget and a choice of hardware
  • This is a Remote (work from home) position.

Most of the machine learning work that reaches production is not modelling. It is knowing which failures matter, building the evidence that shows whether they still happen, and being able to defend that evidence to someone whose job is to find the hole in it.

You will own model development and evaluation on client engagements, largely in regulated settings: federal health, prime contractors, enterprises with data that cannot leave their boundary. That means working against real constraints on residency and access rather than a clean benchmark, and writing up what you found in a form a review board can read.

This role suits someone who has shipped a model that other people depended on, and who has opinions about why aggregate accuracy is a poor summary of anything.

What you will do
  • Design and run evaluations for client models, including refusal behaviour and the failure modes a review board will test
  • Build reproducible training and fine-tuning pipelines against regulated data
  • Turn ambiguous client questions into measurable criteria before any modelling starts
  • Write up methodology and findings for technical and non-technical audiences
  • Review the ground-truth work our annotation teams produce, and feed problems back into the guidelines
What we need from you
  • Three or more years building machine learning systems that other people relied on
  • Fluent Python and the modern ML stack: PyTorch or JAX, Hugging Face, experiment tracking
  • Practical experience evaluating LLMs beyond a single aggregate score
  • Able to explain a technical trade-off to a client who is not an engineer
Nice to have
  • Worked under FedRAMP, HIPAA, or an equivalent compliance regime
  • Published or presented on evaluation methodology
  • Experience with retrieval systems on private corpora