Learning Engineering Intern

Luna builds AI-powered learning experiences that help students think about their thinking—making reasoning visible, coachable, and improvable. We combine learning science, rigorous curriculum design, and product-grade engineering to deliver standards-aligned instruction, practice, and feedback that adapts to learners while staying transparent to teachers and students.

A core part of Luna’s R&D is a bespoke, never-before-done operationalization of conceptual change—merging Jean-François Potvin’s work and Michelene T. H. Chi’s framework(s) into a practical learning-analytics system. This means we’re not just authoring content—we’re building a new way to detect, support, and measure conceptual change in authentic student work.

About the role

We’re hiring a Learning Engineering / Curriculum Development Specialist to design, prototype, and ship high-quality instructional materials and learning flows across K–12 (with an emphasis on math and science). You’ll sit at the intersection of curriculum, assessment, UX, and AI tooling—turning learning objectives into explorable, explainable learning experiences. This is a hands-on builder role: you will write content, design assessments and rubrics, instrument learning data, and collaborate with product/engineering to implement and iterate—with a particular focus on conceptual change and conceptual ecology.

Location: New York City preferred; open to remote (US) / hybrid

Responsibilities

• Design standards-aligned learning progressions (scope/sequence, unit maps, lesson arcs) for core topics.
• Translate learning goals into engaging learning activities: worked examples, practice sets, prompts, tasks, and mini-projects.
• Create “explainable” learning experiences that surface student reasoning (e.g., writing-to-learn, step-by-step justification, reflection prompts).
• Build rubrics and success criteria for both automated feedback and teacher review.
• Help design and iterate Luna’s conceptual change operationalization, translating theory into measurable learning signals and product behaviors.
• Author and structure tasks that reliably elicit student conceptions and misconceptions (diagnostic prompts, explanation tasks, contrasts, bridging analogies, refutation-style activities where appropriate).
• Define concept-level tags, misconception taxonomies, and “change trajectories” that connect curriculum intent → student responses → analytics outputs.
• Work with product/data to ensure conceptual change constructs are represented in the platform (metadata, schemas, logging, evaluation sets).
• Design formative and summative assessments aligned to objectives and cognitive demand.
• Create item specs and item banks (including misconceptions, distractor rationales, partial-credit rules).
• Partner with data/analytics to define measurable learning outcomes and evaluate instructional impact (A/B tests, cohorts, pre/post, mastery models).
• Use AI tools (LLMs, templates, validators) to scale content creation while maintaining quality, accuracy, and pedagogical integrity.
• Define content guidelines, style rules, and review workflows (factual accuracy, bias checks, accessibility, reading level).
• Build and maintain structured content formats (metadata, tags, skill mappings, prerequisite links).
• Work closely with engineering and design to implement content in the product (interactive components, hinting, feedback logic, teacher dashboards).
• Specify how content should behave in the platform: inputs, expected outputs, feedback rules, and edge cases.
• Iterate quickly based on classroom/teacher feedback, student data, and usability testing.
• Design teacher-facing artifacts: lesson guides, facilitation notes, look-fors, differentiation supports, and exemplars.
• Create classroom-ready materials that align with real constraints (time, pacing, heterogeneous skill levels, IEP/ELL supports).

Requirements

• Minimum of 3 years in curriculum development, learning design, or related fields, or an equivalent portfolio.
• Strong command of learning science fundamentals (e.g., retrieval practice, spacing, cognitive load, misconceptions, formative assessment).
• Demonstrated ability to write clear, student-facing instructional content and teacher-facing guidance.
• Experience aligning to standards (e.g., Common Core, NGSS, state standards) and mapping objectives to assessments.
• Comfort working with structured content systems (spreadsheets, CMS, JSON-like schemas, tagging/metadata).
• High attention to detail—accuracy, clarity, and consistency are non-negotiable.
• Collaborative mindset with product/engineering teams; ability to write specs and iterate quickly.

Nice-to-haves

• Experience designing for conceptual change (misconception-driven instruction, explanation-based learning, inquiry learning, knowledge integration).
• Classroom teaching experience (especially math/science, middle or high school).
• Experience with learning analytics, A/B testing, or experimental design in edtech.
• Familiarity with item response theory, mastery learning models, or cognitive diagnostic approaches.
• Experience building interactive digital learning (Desmos-style activities, simulations, inquiry labs).
• Working knowledge of accessibility standards (WCAG), UDL, and culturally responsive pedagogy.
• Comfort with lightweight scripting or data tools (Python/R/SQL) for content validation or analysis.

Work Environment

• Emphasize shipping, measuring, and iterating based on classroom realities and learning data.
• Treat pedagogy as a product, continuously improving through testing and feedback.
• Balance scale with rigor, leveraging AI while maintaining high standards of human expertise.

Benefits

• Learning budget for professional development opportunities.