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Data Annotation Research Jobs in Redmond, WA (NOW HIRING)

Data Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine ...

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Data Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine ...

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Data Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine ...

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Data Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine ...

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Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred Qualifications * Minimum 3 years of experience in private equity, venture capital, investment banking, equity ...

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Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred Qualifications * Minimum 3 years of experience in private equity, venture capital, investment banking, equity ...

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Data Annotation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in strategy consulting, management consulting ...

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Data Annotation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in strategy consulting, management consulting ...

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... data annotation and rubric-based scoring • Prior work in trust and safety, content moderation, QA, or security research • Subject matter expertise in any high-risk domain (cybersecurity ...

You will partner closely with AI/ML research, the ML platform / MLOps function. You own the data side of the contract that defines what a model consumes and emits and annotation, edge, and ...

Staff Data Engineer

Seattle, WA

$130K - $156K/yr

You will partner closely with AI/ML research, the ML platform / MLOps function. You own the data side of the contract that defines what a model consumes and emits and annotation, edge, and ...

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Data Annotation Research information

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.

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.

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.

What job categories do people searching Data Annotation Research jobs in Redmond, WA look for?

The top searched job categories for Data Annotation Research jobs in Redmond, WA are:

What cities near Redmond, WA are hiring for Data Annotation Research jobs?

Cities near Redmond, WA with the most Data Annotation Research job openings:

AI Data Scientist Expert - Remote

YO AI Labs

Seattle, WA • Remote

$100 - $200/hr

Part-time

Posted 2 days ago

New


Job description

Job Title: AI Data Science Domain Expert

Job Type: Contractor (Part-Time)
Location: Remote

Job Overview

We are seeking experienced AI Data Science Domain Experts to contribute their expertise to an innovative project focused on advancing next-generation AI systems. In this role, you will review, evaluate, and refine AI-generated technical and analytical content to improve model accuracy, reasoning, and overall performance. No prior AI experience is required—your data science expertise, analytical thinking, and communication skills are what matter most.

Key Responsibilities
  • Review, edit, and refine AI-generated content for accuracy, clarity, and technical relevance.
  • Develop, optimize, and evaluate prompts to improve AI model performance.
  • Conduct rubric-based assessments of AI outputs and provide structured feedback.
  • Perform independent research and fact-checking to validate technical information.
  • Annotate data and support quality assurance initiatives for AI training.
  • Interpret complex datasets and prepare clear technical reports and summaries.
  • Collaborate remotely with project teams to improve AI models and workflows.
Required Skills
  • Critical Thinking
  • Analytical Reasoning
  • Prompt Engineering
  • AI Output Evaluation
  • Quality Assurance
  • Technical Documentation
  • Technical & Report Writing
  • Content Review & Editing
  • Data Annotation
  • Data Interpretation
  • Fact Checking
  • Independent Research
  • Problem-Solving
  • Attention to Detail
Preferred Qualifications
  • 3+ years of experience in Data Science, Machine Learning, Applied AI, Statistics, Quantitative Analytics, or Data Analytics.
  • Experience producing or reviewing research papers, analytical reports, technical documentation, experiment summaries, or data-driven recommendations.
  • Strong analytical reasoning, critical thinking, and written communication skills.
  • Experience with data annotation, content review, or rubric-based evaluation is preferred.
  • Familiarity with prompt engineering, AI output evaluation, fact-checking, or RLHF is a plus.
  • Master's, MBA, PhD, or other advanced degree is preferred.