• Work directly with internal customers across Sales, Marketing, Support, and Customer Success to turn ambiguous growth and workflow pain into clear product opportunities, shaping solutions that balance technical feasibility with measurable business impact. • Build AI agents and workflows that spot which preschools and childcare centers need help, generate the right message, and guide them from first contact to full, stable use of brightwheel. • Ship beautiful and functional user experiences that make our teams better. • Create tools for support and success that resolve issues faster and turn every interaction into a chance to help small businesses run better. • Instrument what you ship, track metrics like enrollment growth, time saved, and account health, and iterate based on real results. • Shape our data and system architecture so AI can stitch together longitudinal signals and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed and sharing patterns that raise the bar for the whole team.

• Design and build cross-cutting AI services • Own the end-to-end product loop for the problems you take on • Create shared abstractions and tooling for AI • Shape our data and system architecture so AI can safely stitch together longitudinal signals • Lead by example in AI-augmented engineering

• Serve as the technical lead for AI application development within the AI Foundry, setting standards for code quality, architecture, and delivery • Lead by doing: design and implement core AI application components, critical services, and integration layers • Mentor AI engineers; raise the bar on engineering rigor and AI-specific best practices • Establish quality thresholds and release criteria (accuracy, latency, reliability, cost, and user trust) • Design safeguards and “safe failure modes”: fallback behaviors, confidence thresholds, user controls, content filtering, and transparency patterns • Build AI-powered product capabilities end-to-end (service + workflow integration + instrumentation), including LLM-enabled workflows, RAG, summarization, classification, and automation patterns • Build and maintain shared libraries/components for AI application development (prompt/tooling patterns, service templates, evaluation utilities, safety layers) • Own technical readiness for production: reliability, observability, performance tuning, and incident response preparedness • Collaborate with platform Engineering and DevOps to ensure CI/CD and environment consistency, scaling strategies, cost controls for inference and secrets management and secure data handling • Partner tightly with AI product builders and workflow Product owners to translate validated prototypes into production implementations • Collaborate with core engineering teams to integrate AI capabilities into CentralReach’s main platforms • Identify and prioritize foundational investments that increase delivery velocity and reduce long-term maintenance: reusable components, platform primitives, and standardized patterns • Evaluate build vs. buy decisions for AI tooling and recommend approaches aligned to CR constraints • Stay current with AI application engineering practices and help translate emerging techniques into safe, valuable product capabilities

• Engage in rapid prototyping and planning specific to contexts in a cohort of up to 20 leadership teams. • Participate in monthly strategy calls and quarterly in-person intensives. • Interrogate fundamental beliefs about education's structure. • Leverage Playlab's platform to create custom AI tools that enhance human connectivity within education.