Noom combines mobile apps, AI (Claude, OpenAI), and psychology-backed coaching to drive habit change for weight loss and chronic disease management. The tech stack spans React + Kotlin + Java on the client side and Python on the backend, paired with Oracle and SAP for enterprise operations—a hybrid of consumer-grade mobile tooling and healthcare-grade infrastructure. Hiring is heavily weighted toward senior product and engineering roles focused on clinical integration, referral networks, and medication-supported pathways, suggesting a shift from consumer subscription toward enterprise healthcare partnerships and regulated offerings.
Noom is a behavior change platform serving individuals and healthcare organizations. On the consumer side, the product delivers app-based weight-loss courses and a virtual diabetes prevention program (CDC-recognized). On the enterprise side, the platform is embedded in healthcare and pharmaceutical workflows to improve treatment adherence and patient outcomes. The company operates across the United States and internationally, with 1,001–5,000 employees. Active product development focuses on three areas: improving conversion and retention in the consumer funnel (sign-up and purchase experience), scaling clinical operations (study protocols, regulatory compliance, clinical workflows), and advancing AI-driven recommendations and medication-supported interventions. The organization is hiring across product, engineering, research, and finance, with a concentration of senior-level roles.
Frontend: React, Kotlin, Java, TypeScript. Backend: Python. Data/Enterprise: Oracle, SAP. Infrastructure: ArgoCD, Jenkins, Terraform, Pulumi, GitHub Actions. AI: Claude, OpenAI. CRM/HR: Salesforce, Greenhouse.
Core projects: medication-supported behavior change, AI recommendation engine, referral networks with brokers, clinical UX research, study protocol development, and high-impact growth experiments. Pain points include user retention, sign-up and purchase experience optimization, and integrating clinical workflows at scale.
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