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Data Annotation Engineer Jobs in Washington, DC (NOW HIRING)

General Motors

Senior AI/ML Engineer

Annapolis, MD · On-site +1

$103K - $142K/yr

Apply ML to labeling itself Collaborate with ML engineers to design and integrate ML‑driven data annotation (pre‑labeling, autolabeling, active learning loops), helping us move from human‑only ...

General Motors

Senior AI/ML Engineer

Washington, DC · On-site +1

$118K - $162K/yr

Apply ML to labeling itself Collaborate with ML engineers to design and integrate ML‑driven data annotation (pre‑labeling, autolabeling, active learning loops), helping us move from human‑only ...

aqua IT

AI/ML Engineer (Computer Vision)

Herndon, VA · On-site +1

Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

NewGen Technologies Inc.

VLM Engineer

Herndon, VA · On-site

They are seeking a VLM Engineer to design and execute fine-tuning pipelines for Vision-Language ... data annotation platforms and active learning workflows for imagery • Experience with ...

NewGen

VLM Engineer

Herndon, VA · On-site

Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

NewGen Technologies

VLM Engineer

Herndon, VA · On-site

Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

NewGen Technologies

VLM Engineer

Herndon, VA · On-site

Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

teKnoluxion

VLM Engineer

Reston, VA · On-site

Join our growing team as a VLM Engineer . Responsibilities What you get to do every day: * Design ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

teKnoluxion

VLM Engineer

Springfield, VA · On-site

Join our growing team as a VLM Engineer . Responsibilities What you get to do every day: * Design ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

teKnoluxion

VLM Engineer

Reston, VA · On-site

Join our growing team as a VLM Engineer . Responsibilities What you get to do every day: * Design ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

teKnoluxion

VLM Engineer

Reston, VA · On-site

Join our growing team as a VLM Engineer . What you get to do every day: * Design and execute fine ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

B/Core

VLM Engineer with Security Clearance

Reston, VA · On-site

Join our growing team as a VLM Engineer. Responsibilities What you get to do every day: * Design ... Familiarity with data annotation platforms and active learning workflows for imagery * Experience ...

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All JobsData Annotation Engineer JobsData Annotation Engineer Jobs in Washington, DC

Data Annotation Engineer information

See Washington, DC salary details

$58.3K

$167K

$223.1K

How much do data annotation engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for data annotation engineer in Washington, DC is $167,014.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $222,000.00 per year, depending on experience, location, and employer.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are popular job titles related to Data Annotation Engineer jobs in Washington, DC? For Data Annotation Engineer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Data Annotation Engineer jobs in Washington, DC look for? The top searched job categories for Data Annotation Engineer jobs in Washington, DC are:
Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in Washington, DC as of June 2026, with employment types broken down into 71% Full Time, 6% Part Time, and 23% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $167,014 per year, or $80.3 per hour.
Assistant Research Scientist (PREP0004176)

Assistant Research Scientist (PREP0004176)

Johns Hopkins University

Gaithersburg, MD • On-site

Full-time

Posted 2 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 200 frontline employees who took The Breakroom Quiz

223rd of 870 rated healthcare providers

Job description

Description
PREP Research Associate
This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Reliability of Human and LLM Annotations for AI Risk Assessment
The work will entail:
This project focuses on using Large Language Models (LLMs) to provide annotations of evaluation data (a.k.a., LLM as judge), and the design of an Inter-Annotator Agreement study to assess the reliability of both human and LLM annotations. The candidate will explore assessing the indicators of a given AI-related risk, determining how to identify them, and providing annotators with examples to annotate the presence of various risks. The project aims to develop an annotation framework for AI risk assessment and establish metrics for data quality in AI risk research, supporting broader work at NIST in assessing and measuring the validity and reliability of AI-related risks in data annotation.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Gain familiarity with existing literature on data annotation and LLM as judge
  • Understand NIST's role and ongoing efforts in assessing and measuring the validity and reliability of AI-related risks in data annotation
  • Contribute to developing an annotation framework for AI risk assessment
  • Collaborate effectively with cross-functional and interdisciplinary stakeholders to ensure successful project outcomes

Deliverables
  • Contributions to a NIST report that supports ongoing NIST AI evaluation efforts focused on the design of an Inter-Annotator Agreement to assess the reliability of both human and LLM annotations.

Qualifications
  • Background in Computer Science, Data Science, or related field.
  • Education level: Bachelor's or Graduate Degree
  • Strong interest in data annotation and AI risks
  • Familiarity with scientific reading and technical writing

Application Instructions
Please upload the following with your application:
• CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender
Privacy Act Statement
Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.

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