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Data Annotation Services Jobs in Silver Spring, MD

Innodata Inc.

QA / Evaluation Lead

Washington, DC · On-site

$45 - $50/hr

Over an intensive 20-week phase, you'll help stand up a data services storefront, a DataCard ... Design and implement confidence-threshold escalation routing from automated annotation to senior ...

BAE Systems

INTEL ANALYST

Fort Belvoir, VA · On-site

Responsibilities for this position include providing imagery and geospatial analysis services in ... Experience with Machine Learning training data creation, annotation, and review as well as ...

BAE Systems

INTEL ANALYST

Fort Belvoir, VA

Responsibilities for this position include providing imagery and geospatial analysis services in ... Experience with Machine Learning training data creation, annotation, and review as well as ...

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Data Annotation Services information

What are data annotation services?

Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.

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

To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.

What are some common challenges faced when working in data annotation services, and how can I address them?

In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.

What is the difference between Data Annotation Services vs Data Labeling Specialists?

AspectData Annotation ServicesData Labeling Specialists
CredentialsTypically no formal credentials required; focus on trainingOften have training in specific tools or industry standards
Work EnvironmentCollaborative, often remote or in-office teamsSimilar, working in teams or independently on labeling tasks
Industry UsageUsed by AI/ML companies for training datasetsEmployed in similar settings, focusing on labeling data for AI models
Search & Comparison IntentUnderstanding services offered for data preparationLooking for roles or tasks related to data labeling

Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.

What are popular job titles related to Data Annotation Services jobs in Silver Spring, MD?

For Data Annotation Services jobs in Silver Spring, MD, the most frequently searched job titles are:

What job categories do people searching Data Annotation Services jobs in Silver Spring, MD look for?

The top searched job categories for Data Annotation Services jobs in Silver Spring, MD are:

What cities near Silver Spring, MD are hiring for Data Annotation Services jobs?

Cities near Silver Spring, MD with the most Data Annotation Services job openings:

Data Annotation Services jobs near you
Infographic showing various Data Annotation Services job openings in Silver Spring, MD as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution.

QA / Evaluation Lead

Innodata Inc.

Washington, DC • On-site

$45 - $50/hr

Full-time

Re-posted 24 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

166th of 247 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 QA/Evaluation Lead, you'll own quality and evaluation across the platform. You'll design the evaluation framework that measures whether our data services and outputs meet the bar, build repeatable test and validation processes, and give the team an objective read on readiness at each milestone. Partnering with the Delivery Owner and engineering leads, you'll turn quality from an afterthought into a measurable, demonstrable strength. It's a role for someone who thinks rigorously about evaluation and takes pride in evidence-backed quality.

Key Responsibilities:

  • Design and own the inter-annotator agreement (IAA) methodology for the Phase 1 demonstration corpus - metric selection (Cohen's kappa, Fleiss, Krippendorff's alpha), sampling design, adjudication workflow, and agreement thresholds
  • Define evaluation framework architecture: test and evaluation plans, IAA targets, drift detection gates, and model performance metrics per SOW Section 2.9
  • Configure and operate sampling-based quality control across the self-service and white-glove annotation paths during Phase D corpus production
  • Design and implement confidence-threshold escalation routing from automated annotation to senior-annotator adjudication
  • Validate quality scoring and IAA computation within the Innodata data layer
  • Support AI Solutions Engineer on evaluation design for SAM 2 and Frontier model API validation - define what 'good enough' looks like quantitatively
  • Produce evaluation framework documentation for the Phase 1 NPP closeout package, including per-DataCard documentation with the SA

Must-Have Qualifications:

  • Bachelor's degree in Statistics, Data Science, Computer Science, or related quantitative 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 in data quality, evaluation methodology, or QA on AI/ML programs
  • IAA methodology expertise - Cohen's kappa, Fleiss' kappa, Krippendorff's alpha: hands-on, not theoretical
  • Evaluation framework design for AI/ML training data programs
  • QC process design: sampling methodology, escalation workflows, adjudication protocols
  • Python for QC tooling, metric computation, and statistical analysis
  • Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

  • Prior DoD or IC data quality program experience
  • CVAT or equivalent annotation platform QC workflow configuration
  • Drift detection and model monitoring methodology
  • Experience with FMV / video annotation quality standards

The expected hourly salary range for this position is $45 to $50 p/hour, based on experience, skills, and qualifications.

Note to Candidates: 

This role is not a project manager with QC responsibilities - it is a methodology expert who owns the intellectual framework behind data quality on a federal AI program. The right candidate can walk into a meeting with Government evaluators and explain exactly why the evaluation design produces trustworthy labels. That conversation is part of Phase 2 positioning.

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