Modulo is seeking a creative, knowledgeable, and engaging Machine Learning tutor to support homeschool students in understanding AI and ML—not just as buzzwords, but as meaningful, hands‑on learning experiences. This is an opportunity to guide learners through building, testing, and reflecting on ML models in a developmentally appropriate way.
Apply here:
https://joinmodulo.com/pages/online-tutoring-jobs
This is a fully remote, contract-based position. Tutors may be located anywhere, provided they are reliable, communicative, and dedicated to helping students grow. Strong applicants may be considered for roles beyond machine learning support as well.
About Modulo
Modulo is a tutoring and educational support platform for homeschoolers and families creating tailored alternatives to traditional school. We match each student with tutors, classes, and curriculum based on their unique learning profile. Many of our learners are gifted, twice‑exceptional, or benefit from flexible, interest-led education. Families seek tutors who can teach machine learning with both rigor and creativity.
About You
- You have experience teaching or mentoring kids or teens in machine learning, AI literacy, or related coding/tech subjects.
- You use evidence-backed methods: hands-on experimentation, model training and testing, reflective discussion, and ethical framing.
- You introduce machine learning via real-world, kid-friendly examples (recognizing images, recommender systems, simple classifiers).
- You help students collect, label, and experiment with datasets; build simple classifiers; observe model performance metrics, and reflect on results. Research shows this data-driven approach supports creativity, confidence, and deeper understanding .
- You can explain why models may fail or behave unexpectedly and encourage students to question bias or limitations—integrating ethics as a core topic, not an afterthought .
- You use visual tools or block-based environments (like Scratch, Cognimates, or ML‑Quest) to engage younger learners, and scaffold toward Python-based tools (e.g. Teachable Machine, MachineLearningForKids) for older students .
- You are a clear and patient communicator who adapts your teaching approach to each learner’s goals, age, and interests.
- You know how to teach machine learning in a way that’s fun, collaborative (encouraging peer review or model sharing), and age-appropriate.
- You are compassionate, organized, reliable, and committed to supporting students and families with professionalism and care.
Responsibilities
- Teach one-on-one online sessions introducing machine learning in developmentally appropriate ways.
- Guide students to create and label datasets and train simple ML models (such as image or text classifiers), visualize outcomes, and interpret model metrics.
- Use interactive, visual, or block‑coding tools for younger learners to foster engagement and intuition.
- Introduce older students to Python-based ML tools or browser-based platforms with guided scaffolding.
- Frame lessons around real-world examples students understand (e.g. fruit recognition, game mechanics, recommendation systems).
- Model the full learning cycle: problem formulation, data preparation, model building, evaluation, reflection, and iteration.
- Encourage students to think critically about model performance, bias, and limitations.
- Incorporate collaborative or reflective elements, such as swapping models, comparing outcomes, or discussing what changed and why .
- Frame ethics and responsible AI as central: help students question where data comes from, what assumptions are built into models, and how outputs may affect others.
- Communicate progress clearly with families, including upcoming goals and student reflections.
Qualifications
- Experience teaching or tutoring machine learning, AI literacy, or computational thinking concepts to kids or teens—especially online.
- Knowledge of age-appropriate ML tools and platforms: Scratch-based environments, Teachable Machine, Machine Learning for Kids, Cognimates, or similar.
- Understanding of research-backed pedagogy for kids learning ML, including hands-on experimentation, reflection, and ethical framing .
- Proven ability to scaffold from simple, concrete experiences for younger students to more advanced tasks for older learners.
- Comfortable working with diverse learners, including gifted, neurodivergent, or self-directed students.
- Clear and patient communicator with strong capacity to adapt lessons to individual interests and developmental levels.
- Organized, punctual, and consistent in working with families and students.
What Sets a Great Modulo Tutor Apart
- Exceptional tutors act as guides—not just lecturers. They:
- Help students build hands-on, engaging ML experiences instead of memorizing concepts.
- Encourage students to experiment, reflect, compare their models, and iterate.
- Incorporate privacy, bias, fairness, and ethical considerations into conversations—not as add-ons.
- Use tools and examples that connect to students’ interests (games, imagery, stories).
- Balance learner agency with structured guidance, fostering both creativity and conceptual depth.
- Celebrate curiosity, critical thinking, and growing independence in students.
Student Ages and Matching
Our students range from upper elementary through high school and occasionally early college. Tutors will be matched based on your machine learning expertise, preferred age group(s), and teaching strengths.
Schedule
Tutors set their own availability. Families are matched based on subject fit, scheduling alignment, and teaching approach. Tutors may work with one or more students depending on demand.
How to Apply
If this role aligns with your experience and interests, apply now:
https://joinmodulo.com/pages/online-tutoring-jobs
You’ll be asked to share your availability and submit a short video introducing yourself and your teaching approach. We use this information to match you with families who are looking for thoughtful, hands-on machine learning instruction.
Job Types: Part-time, Temporary
Pay: $47,708.47 - $57,455.36 per year
Expected hours: 1 – 5 per week
Benefits:
- Flexible schedule
Work Location: Remote
