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Data Annotation Jobs in Laurel, MD (NOW HIRING)

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

You'll integrate synthetic data generation into the pipeline, stand up and tune the annotation toolchain, and orchestrate reproducible ML workflows that the rest of the team can build on. You'll ...

Welo Data

Data Labeling Associate

Washington, DC

$34/hr

Execute Data labelling and annotation tasks across speech and voice datasets. * Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A FIT IF YOU'RE... * A ...

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Data Annotation information

What does a data annotation do?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

How much money can I make doing data annotation?

Data annotation jobs typically pay between $10 and $20 per hour, depending on the complexity of the task and the employer. Experienced annotators or those working on specialized projects may earn higher rates, especially if they have skills in specific tools or domains. Earnings can vary based on whether the work is freelance, part-time, or full-time, and some platforms offer bonuses for accuracy or speed.

What is a data annotation?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

What are the key skills and qualifications needed to thrive in data annotation?

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What are the most commonly searched types of Data Annotation jobs in Laurel, MD? The most popular types of Data Annotation jobs in Laurel, MD are:
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Data Annotation jobs near you
Infographic showing various Data Annotation job openings in Laurel, MD as of August 2026, with employment types broken down into 65% Full Time, 9% Part Time, and 26% Contract. Highlights an 73% In-person, 5% Hybrid, and 22% Remote job distribution.

AI Solutions Engineer

Innodata Inc.

Washington, DC

$75 - $80/hr

Full-time

Re-posted 2 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

161st of 243 rated software companies

Job description

About the Program: 

Innodata's Federal Practice builds the trusted data layer for critical infrastructure Trust & Safety work. Partnering with a leading systems integrator, we're delivering a modern, governed data services platform in a secure federal (IL4) environment. Over an intensive 20-week phase, you'll help stand up a data services storefront, a DataCard governance framework, synthetic data integration, and Databricks write-back capabilities.

About the Role: 

As the AI Solutions Engineer, you'll bring the platform's AI capabilities to life. You'll integrate synthetic data generation into the pipeline, stand up and tune the annotation toolchain, and orchestrate reproducible ML workflows that the rest of the team can build on. You'll partner with the Solution Architect and Data/Annotation Engineer to turn raw corpora into high-quality, model-ready data. This role suits an engineer who's fluent across modern AI tooling and enjoys making sophisticated ML infrastructure actually work in production.

Key Responsibilities:

  • Configure and validate native AI-assistive features across bundled platform components (Dataset Explorer, DataCard Service, Annotation Platform)
  • Integrate and tune SAM 2 for full-motion video annotation: object tracking, segmentation calibration, confidence threshold configuration
  • Implement Frontier model API integration for synthetic data fidelity validation: prompt engineering, response validation, quality scoring
  • Configure AI-assisted annotation features: confidence scoring, auto-escalation triggers, model-assisted label suggestion
  • Implement ICAM / OIDC authentication integration with AFS identity framework
  • Configure data-layer DLP policies above the AFS-managed DLP infrastructure substrate
  • Configure NiFi FMV codec validation layer (H.264, H.265, MPEG-4) above AFS-managed substrate
  • Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write-back during Phase C

Must-Have Qualifications:

  • Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis.
  • 6+ years total professional experience, 4+ years hands-on AI/ML engineering
  • SAM 2 or equivalent foundation model integration for computer vision or video annotation
  • Frontier model API integration (OpenAI, Anthropic, or equivalent): async job management, quality validation pipelines
  • Python - strong, production-grade; comfortable with ML tooling and data pipeline development
  • Experience configuring AI-assistive features in annotation platforms or ML data tooling
  • Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

  • CVAT annotation platform - AI feature configuration and operation
  • DoD or IC data program experience: CUI, distribution statements, federal data governance
  • Evaluation design for AI/ML training data: IAA methodology, drift detection, model performance measurement
  • Video understanding or FMV annotation experience
  • DataCard or ML data provenance framework familiarity

The expected hourly salary range for this position is $75 to $80 p/hour, based on experience, skills, and qualifications.

Note to Candidates: 

This role does not own infrastructure deployment. The AI Solutions Engineer operates at the AI/ML configuration and integration layer above the infrastructure. Ideal candidate is equally comfortable writing Python integration code and reasoning about model quality - and understands that in a federal data environment, every AI decision needs an audit trail.

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