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Data Labeler Remote Jobs in Crofton, MD (NOW HIRING)

10a Labs

Junior Cyber Investigator

Washington, DC · Remote

$80K - $105K/yr

Review and label AI-generated exchanges to assess whether content provides meaningful offensive ... data extraction, ransomware, local and remote exploits, or offensive security operations

10a Labs

Senior Cyber Investigator

Washington, DC · Remote

$114K - $140K/yr

Review and label AI-generated exchanges, and support label quality across the team to help tune ... data extraction, ransomware, local and remote exploits, or offensive security operations * Strong ...

Welo Data

English AI Maps Trainer

Baltimore, MD · Remote

$20.30/hr

... Labels too small"). YOU ARE A FIT IF YOU...   * Have an eye for artistic detail and intuitive ... Remote · Estimated volume: 8 - 10 hours · Start date: The project runs on a weekly basis.

Eliassen Group

Email Cyber Security Engineer

Rockville, MD · Remote

$70 - $80/hr

Hybrid 3 days onsite / 2 days remote in either Rockville, MD or Tysons Corner, VA Our client seeks ... Drive data classification and labeling initiatives and integrate with DLP technologies. * Manage ...

System One

Purview Administrator

Greenbelt, MD · Remote

Remote, with optional onsite work in Washington, DC for local candidates Employment Type: Contract ... labeling, retention, and data loss prevention policies. * Set up and maintain user accounts, role ...

System One

Purview Administrator

Greenbelt, MD · Remote

Remote, with optional onsite work in Washington, DC for local candidates Employment Type: Contract ... labeling, retention, and data loss prevention policies. * Set up and maintain user accounts, role ...

System One

Purview Administrator

Greenbelt, MD · Remote

Remote, with optional onsite work in Washington, DC for local candidates Employment Type: Contract ... labeling, retention, and data loss prevention policies. * Set up and maintain user accounts, role ...

System One

Purview Administrator

Greenbelt, MD · Remote

Remote, with optional onsite work in Washington, DC for local candidates Employment Type: Contract ... labeling, retention, and data loss prevention policies. * Set up and maintain user accounts, role ...

System One

Purview Administrator

Greenbelt, MD · Remote

Remote, with optional onsite work in Washington, DC for local candidates Employment Type: Contract ... labeling, retention, and data loss prevention policies. * Set up and maintain user accounts, role ...

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All JobsData Labeler JobsData Labeler Remote JobsData Labeler Remote Jobs in Crofton, MD

Data Labeler Remote information

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$15

$39

$57

How much do data labeler remote jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for data labeler remote in Crofton, MD is $39.12, according to ZipRecruiter salary data. Most workers in this role earn between $34.28 and $44.23 per hour, depending on experience, location, and employer.

What does a remote data labeler do?

A remote data labeler is responsible for annotating or tagging data—such as images, videos, audio, or text—from a remote location, typically working from home. Their work helps train machine learning models by providing accurate, labeled datasets that algorithms use to learn and make predictions. Data labelers follow specific guidelines to ensure consistency and accuracy, and may use specialized software tools to complete their tasks. This role is essential in industries like artificial intelligence, self-driving cars, and natural language processing. Remote data labelers often work as freelancers or as part of distributed teams for tech companies.

What are the key skills and qualifications needed to thrive as a remote data labeler?

To thrive as a Data Labeler Remote, you need strong attention to detail, basic data analysis skills, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Proficiency with labeling platforms, annotation tools, and sometimes knowledge of spreadsheet software are typically required. Reliability, time management, and effective communication are crucial soft skills for maintaining accuracy and meeting project deadlines in a remote setting. These skills ensure high-quality, consistent labeled data, which is essential for training reliable machine learning models.

What are some common challenges faced by remote data labelers and how can they be managed?

Remote data labelers often encounter challenges such as maintaining focus during repetitive tasks, ensuring consistent annotation quality, and communicating effectively with distributed teams. To manage these, it's helpful to establish a structured work routine, take regular breaks to prevent fatigue, and use annotation guidelines provided by employers. Leveraging collaboration tools for feedback and clarification also helps maintain high-quality output and fosters a sense of connection with team members.

What is the difference between Data Labeler Remote vs Data Annotator Remote?

AspectData Labeler RemoteData Annotator Remote
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageCommon in AI/ML data preparationCommon in AI/ML data preparation
Job FocusLabeling data points for machine learningAnnotating data for training AI models

Both Data Labeler Remote and Data Annotator Remote roles involve preparing data for AI and machine learning projects. While the terms are often used interchangeably, Data Labeler Remote typically emphasizes labeling data points, whereas Data Annotator Remote may include more detailed annotation tasks. Both roles require similar skills and are performed remotely, making them accessible for individuals seeking flexible data-related jobs.

How much does a data labeler remote make?

Remote data labelers typically earn between $12 and $20 per hour, depending on experience, complexity of tasks, and the company. Some positions may offer additional incentives or flexible schedules, but wages generally align with entry-level data annotation roles in the industry.

Is data labeling a good career?

Data labeling is a common entry-level role in the AI and machine learning industry, involving annotating data such as images, text, or audio to train algorithms. It often offers flexible remote work options and requires attention to detail but typically has lower barriers to entry and limited career advancement without additional skills or certifications.

What job categories do people searching Data Labeler Remote jobs in Crofton, MD look for?

The top searched job categories for Data Labeler Remote jobs in Crofton, MD are:

What cities near Crofton, MD are hiring for Data Labeler Remote jobs?

Cities near Crofton, MD with the most Data Labeler Remote job openings:

Data Labeler Remote jobs near you

Senior Data Scientist, AI Retrieval Systems

Axle

Rockville, MD • Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago

Job description

(ID: 2026-3395)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

  • 100% Medical, Dental & Vision Coverage for Employees
  • Paid Time Off and Paid Holidays
  • 401K match up to 5%
  • Educational Benefits for Career Growth
  • Employee Referral Bonus
  • Flexible Spending Accounts:
    • Healthcare (FSA)
    • Parking Reimbursement Account (PRK)
    • Dependent Care Assistant Program (DCAP)
    • Transportation Reimbursement Account (TRN)

Axle is seeking a Senior Data Scientist, AI Retrieval Systems to join our vibrant team supporting rare disease research at the National Institutes of Health (NIH). This is a Remote position within the United States. 

Position Summary:

Roughly 25 to 30 million people in the United States live with a rare disease. There are somewhere between 7,000 and 10,000 distinct rare conditions, and the large majority have no FDA-approved treatment. 

Research on these conditions keeps running into the same obstacles. Published evidence for any one disease is thin and scattered across sources. The same clinical finding gets written down a dozen different ways depending on who recorded it. And the people with the most at stake, patients and their families, are usually the least equipped to read the specialist literature written about their own condition. 

Large language models are well suited to this class of problem, and the research programs we support are investing in applying them carefully. In this role you will build the retrieval and knowledge layer that those AI systems stand on. That means the disease and phenotype vocabularies that give a model something precise to reason over, the semantic search that finds the right concept behind an imprecise human phrase, and the ranking that decides what a user sees first. Ontologies serve as internal scaffolding throughout. Users should never have to see one or learn what it is. 

This is a senior individual contributor position with unusual range. You will own the data layer, the retrieval services built on top of it, the interfaces where results become visible, and the path onto the computing infrastructure that runs it all. You will work directly with NIH program staff, clinical geneticists, and rare disease information specialists. 

Core Responsibilities:

  • Model biomedical knowledge for rare disease research. Ingest disease and phenotype ontologies and controlled vocabularies into PostgreSQL with a maintainable release and refresh path, reconcile identifiers across sources, and work through term hierarchies to determine what is clinically relevant for a given condition. 

  • Build retrieval-augmented services that ground everyday language in clinical concepts. Embed term labels, definitions, and synonyms, retrieve candidates, and have a model disambiguate against context before any value is committed.

  • Treat retrieval as a database problem. Tune keyword and vector search over large biomedical corpora, and be ready to defend the recall and latency trade-offs you choose.

  • Build the ranking and relevance layers that decide what surfaces first, including domain-aware weighting and graceful degradation when a condition falls outside curated coverage.

  • Deliver the interfaces where this work becomes visible to users, in Next.js, React, and TypeScript. This covers question and confirmation flows, result presentation, and live status for long-running pipelines.

  • Deploy continuously onto NIH on-premises and high-performance computing Kubernetes environments. Helm charts, StatefulSets, secrets, ingress, GPU scheduling for self-hosted inference, and scheduled jobs are all in scope, and you will partner with the operations teams that run those environments instead of standing up parallel cloud infrastructure.

  • Build the evaluation that tells us whether retrieval and concept mapping are good enough to rely on, and keep it running as a regression suite instead of a one-time measurement.

  • Log what the system does and why. Request identifiers, latency, errors, and which concept the system selected all need to be captured, so that staff can review an AI-assisted result instead of taking it on faith.

  • Work out what researchers, clinicians, and patient communities need, and turn it into data models, retrieval behavior, and interface design.

  • Write the work up. You will contribute to manuscripts, conference abstracts, and posters with NIH investigators, and you will be credited as an author on work you helped produce.

Required Qualifications:

  • Bachelor's degree in Data Science, Computer Science, Bioinformatics, Biomedical Informatics, or a related field. An advanced degree is preferred. We will consider equivalent professional experience in place of a degree.

  • At least 5 years building and operating production software or data systems. At least 2 of those years should involve shipping LLM-powered applications (agents, retrieval, or evaluation) that people depend on. We weigh depth in retrieval and applied LLM engineering more heavily than total years.