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Data Annotation Research Jobs in Virginia (NOW HIRING)

Seekr

Senior Data Scientist

Reston, VA · On-site

Coordinate data collection and annotation efforts for supervised training efforts * Design and implement tests for machine learning algorithm effectiveness and performance monitoring * Research and ...

Seekr

Senior Data Scientist

Reston, VA · On-site

Coordinate data collection and annotation efforts for supervised training efforts * Design and implement tests for machine learning algorithm effectiveness and performance monitoring * Research and ...

BlackSky

Staff Engineer, AI

Herndon, VA · On-site +1

Collaborate with the Data QA team to define annotation standards, resolve taxonomy issues, and ... Experience researching, building, and evaluating production-ready CV models for detection ...

CoVar

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

... ML R&D software company with offices in Durham, NC and McLean, VA, that uses artificial ... Experience with data curation/annotation workflows and dataset quality control. * Software ...

BlackSky

Staff Engineer, AI

Herndon, VA · On-site

Collaborate with the Data QA team to define annotation standards, resolve taxonomy issues, and ... Experience researching, building, and evaluating production-ready CV models for detection ...

BlackSky

Staff Engineer, AI

Herndon, VA · On-site

... Data QA team to define annotation standards, resolve taxonomy issues, and identify data-quality ... researching, building, and evaluating production-ready CV models for detection, segmentation ...

BlackSky

Staff Engineer, AI

Herndon, VA · On-site

... Data QA team to define annotation standards, resolve taxonomy issues, and identify data-quality ... researching, building, and evaluating production-ready CV models for detection, segmentation ...

All JobsData Annotation Research JobsData Annotation Research Jobs in Virginia

Data Annotation Research information

What qualifications do I need for data annotation?

Data annotation research roles typically require basic computer skills, attention to detail, and familiarity with annotation tools or platforms. A high school diploma or equivalent is usually sufficient, though some positions may prefer experience with data labeling, machine learning concepts, or specific software. Strong communication skills and the ability to work independently are also beneficial.

What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

Does data annotation actually pay?

Data annotation research jobs typically pay hourly or per task rates, with wages ranging from minimum wage to higher rates depending on experience and complexity of the work. Many positions are freelance or remote, requiring basic skills in data labeling tools and attention to detail. Payment is generally reliable, but rates vary by employer and project.

How hard is it to get hired by data annotation?

Getting hired for a data annotation research role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible for those with the right skills and reliability.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation jobs require attention to detail and often use tools like labeling platforms or software, making them a legitimate employment opportunity in the tech industry.

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.
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What cities in Virginia are hiring for Data Annotation Research jobs? Cities in Virginia with the most Data Annotation Research job openings:
Data Annotation Research jobs near you

Senior Data Scientist

Seekr

Reston, VA • On-site

Other

Posted 26 days ago

Job description

About the Opportunity: 

We are looking for talented Data Scientiststo join our team and deliver generative and predictive capabilities for US Government customers.  The right candidate will work on the design, development, and deployment of AI/ML capabilities for various applications implementing generative AI and integrations with predictive capabilities (agentic workflows, LLM fine-tuning).  Additionally, this person will be working on quick proof-of-concept solutions but also building in a generalized fashion to support longer term implementation.

From your first day, you will make a valuable contribution. We are a fast-growing company where no one is a bystander. We offer you the opportunity to make a meaningful impact for the US while designing and developing cutting-edge, novel capabilities.

Active US Security clearance or eligibility and willingness to obtain a US Security clearance. Top Secret with SCI eligibility highly preferred. 

Duties and Responsibilities: 

  • Design, implement, and deploy machine learning algorithms for USG customers 
  • Design and develop prototype solutions incorporating both generative and predictive capabilities
  • Serve as the subject matter expert on cutting-edge generative techniques to enhance the government team's ability to produce meaningful insights
  • Design, implement, and manage machine learning data pipelines
  • Conduct research and development on agentic integration of capabilities and application to USG use cases
  • Interact directly with customers and subject matter experts, translating AI/ML concepts into practical language
  • Manage machine learning algorithm lifecycle
  • Support pre-sales efforts, identifying how the Seekr Platform could help satisfy customer requirements
  • Coordinate data collection and annotation efforts for supervised training efforts
  • Design and implement tests for machine learning algorithm effectiveness and performance monitoring
  • Research and development on cutting-edge machine learning technologies

Qualifications and Skills: 

  • Graduate degree in Computer Science preferred with a strong background in machine learning required
  • Strong problem-solving abilities, solid background in algorithms and data structures required 
  • Comfortable interacting with customers and subject matter experts, translating complex AI/ML concepts into practical language and impacts
  • Familiar with generative AI concepts like RAG, prompt engineering, agents, structured output, multi-modal LLMs, various embedding models
  • Strong programming skills in Python required. Experience in java and html a plus. 
  • Experience working with or in the DoD, US Intelligence Community, or US Federal Civilian agencies
  • Solid knowledge of machine learning tools (eg. scikit-learn, tensorflow, keras, pytorch, Spark MLlib) required 
  • Solid knowledge of LLM Frameworks such as LangChain, LlamaIndex,pydantic Transformers
  • Familiarity with common APIs including OpenAI, GraphQL, FastAPI
  • Familiarity with Databases and Retrieval Augmented Generation (RAG) technologies such as Pinecone, FAISS, OpenSearch, and Neo4j
  • Experience with interactive machine learning (eg. active learning, reinforcement learning, machine teaching) a plus but not required

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