Kiddom builds an AI-assisted curriculum and instruction platform for K–12 teachers, powered by a ML stack (scikit-learn, XGBoost, TensorFlow, PyTorch) that spans personalization, recommendation, and agentic lesson planning. The tech mix—React/Next.js frontend with Go and Python backend—reflects active work on search systems and scalable frontend architecture. Engineering dominates hiring (30 roles), with senior and director-level concentration, suggesting a phase of either product deepening or architectural debt reduction as they scale AI features.
Notable leadership hires: Director of Engineering
Kiddom serves K–12 schools and districts with a platform that integrates standards-aligned curriculum, AI-assisted lesson planning, automated grading, and data dashboards for teachers. Founded in 2015 and based in San Francisco, the company operates at 201–500 employees. The product strategy centers on reducing teacher prep work while surfacing data-driven insights tied to student outcomes. Current initiatives include agentic assistants for lesson planning, intelligent discovery and recommendation pipelines, and state curriculum adoption support, alongside internal scaling work on legacy data systems and frontend performance.
Frontend: React, TypeScript, Next.js, Angular, Vue. Backend: Go, Python, Java, Clojure. ML: scikit-learn, XGBoost, TensorFlow, PyTorch, PEFT, LoRA, RLHF. Data: PostgreSQL, MySQL, MongoDB, NoSQL, Redis. Infrastructure: AWS, Docker, Linux, Terraform.
AI-powered lesson planning assistants, search and recommendation systems, intelligent discovery pipelines, personalization integration, state curriculum support, and scalable frontend architecture. Also addressing legacy data migration and go-to-market for product launches.
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