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Education Subject Matter Expert (SME) with AI trainer – Remote – 1099 Only Location: Remote within USA
Title : Education Subject Matter Expert (SME) with AI trainer – Remote – 1099 Only
Location: Remote within U.S
*** This is a fully remote 1099 contract opportunity for an estimated 3‑month engagement.
20-35 hours in a week
Job Description:
Role Overview
We are seeking an experienced Education Subject Matter Expert (SME) with sharp analytical skills and a deep understanding of pedagogical frameworks, curriculum development (e.g., K-12 standards, higher education), learning science, and assessment design to join our AI training and model development team.The ideal candidate will bring broad expertise across multiple educational domains—from K-12 content creation and assessment to advanced academic research and curriculum design—to help train, evaluate, and fine-tune AI models. Your goal is to ensure the highest levels of pedagogical accuracy, grade-level appropriateness, content alignment with established curricula, and effective knowledge transfer within the AI’s output.
You will work with cross-functional teams of data scientists, AI engineers, and product teams to build intelligent educational solutions such as automated lesson plan generators, dynamic study guides, quiz/exam creation tools, and academic research assistants.
Project Goal & Scope: The goal is to construct high-quality datasets that represent realistic, end-to-end educational workflows and professional skills. These datasets will train and test the model's ability to act as a competent and helpful Educator, Curriculum Designer, or Academic Researcher.
We are pursuing a dataset that represents work beyond simple content retrieval; instead, it should demonstrate real-world use cases performed by mid and senior-career education experts, such as generating lesson plans, designing high-stakes assessments, and synthesizing complex academic content for various target audiences (from high school students to academic peers).
Key Responsibilities
- Domain Oversight: Serve as the primary educational authority during the training and fine-tuning of AI models, ensuring all outputs adhere to pedagogical standards, grade-level appropriate complexity (Lexile/Grade Level Fit), and correct educational terminology.
- Knowledge Architecture: Develop knowledge bases, academic taxonomies, and ontologies that reflect multi-disciplinary standards and complex curricular hierarchies.
- Data Validation: Annotate, label, and validate datasets with specific educational knowledge (e.g., curriculum mapping standards, clear learning objectives, and appropriate complexity for target audiences).
- Prompt Engineering & Strategy: Collaborate with AI/ML engineers to design training data strategies and prompt engineering techniques specifically tailored for educational reasoning and content creation, including generating explainer texts, designing assessment rubrics, and originating lesson plans or academic papers.
- Model Audit: Provide expert feedback on model outputs, identifying "hallucinations," factual inaccuracies, or pedagogical flaws to improve the model’s reliability in instructional and assessment environments.
- Business Alignment: Partner with product teams to align AI capabilities with educational objectives, such as automating curriculum alignment or supporting personalized learning.
- Professional Watch: Stay current with emerging trends in Educational Technology (EdTech) and learning science research to ensure ethical and effective model implementation.
- Education: Advanced degree (Master's or PhD) in Education, a specific academic discipline, Curriculum & Instruction, or a related field. Teaching certification or equivalent experience is a plus.
- Experience: 10–20 years of experience in the education sector (e.g., teaching, curriculum design, academic research, or educational content development).
- Analytical Precision: Proven ability to translate complex educational standards and learning objectives into structured logic and data requirements.
- Communication: Exceptional writing and verbal skills, with the ability to bridge the gap between technical AI teams and pedagogical/academic stakeholders.
- Systems Knowledge: Familiarity with EdTech stacks such as LMS (Learning Management Systems), digital assessment platforms, or academic research databases (e.g., JSTOR, EBSCO).
