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Data Science Specialist - AI Trainer Project- Freelance AI Trainer Project

Agency - United States of America

Posted Jun 7, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
$6-$65/hr From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

37% below the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Mid From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Data Science Specialist - AI Trainer Project- Freelance AI Trainer Project United States of America Are you a data science expert eager to shape the future of AI? Large‑scale language models are evolving from clever chatbots into powerful engines of analytical discovery. With high‑quality training data, tomorrow's AI can democratize world‑class education, keep pace with emerging technologies, and streamline data-driven decision-making for professionals everywhere. That training data begins with you-we need your expertise to help power the next generation of AI. We're looking for data science specialists who live and breathe machine learning, statistical modeling, data engineering, data visualization, natural language processing, time series analysis, deep learning, and algorithm development. You'll challenge advanced language models on topics like supervised learning, unsupervised clustering, regression techniques, model evaluation metrics, feature engineering, anomaly detection, and real-world data pipelines-documenting every failure mode so we can harden model reasoning. On a typical day, you will converse with the model on project scenarios and theoretical data science questions, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics. A master's or PhD in data science, computer science, statistics, or a closely related field is ideal; peer‑reviewed publications, industry projects, experience with cloud platforms, or open-source contributions signal fit. Clear, metacognitive communication-“showing your work”-is essential. Ready to turn your data science expertise into the knowledge base for tomorrow's AI? Apply today and start teaching the model that will teach the world. We offer a pay range of

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