• Architect the 'Agent-Ready' infrastructure, building the sandboxed environments and headless interfaces that allow AI agents to execute safely without human hand-holding • Embed with product teams to identify friction in the 'Plan → Code → Test' loop, shipping 0-to-1 internal tools that ruthlessly automate bottlenecks • Replace manual human validation with deterministic proof; build the validation frameworks that allow agents to verify their own work • Act as a technical scout for the organization, critically evaluating emerging agentic frameworks to inform our build-vs-buy strategy

• Driven and excited about challenges of delivering complex projects in production with a focus on quality • Customer focused - making customers more successful by taking on their most challenging problems motivates you • Have a strong sense of teamwork and able to make decisions which benefit the team and company • Identify root causes, and get beyond treating symptoms - motivated to dig deep and solve hard problems

• Build end-to-end software that brings Cherry into more practices and launch new products beyond BNPL. • Make architectural decisions that shape Cherry's platform across frontend, backend, integrations, and infrastructure • Define and implement observability, monitoring, and metrics systems that make our products safer, faster and more reliable for practices and patients. • Collaborate with product, design, data, and operations to rapidly deliver high-impact features and experiment toward measurable outcomes.

• Build full-stack product features end-to-end: frontend UI, backend APIs/services, data pipelines, deployment and observability. • Own the integration strategy and connector architecture across multiple vendors and authentication patterns (OAuth/API tokens/read-only portal access/automated exports). • Design and implement data ingestion and processing primitives: retries, idempotency, backfills, schema evolution, and anomaly detection. • Create a flexible canonical data model and mapping layer that can normalize structural + semantic differences across systems (e.g., different definitions of revenue/net sales/business day). • Build the metrics/semantic layer that powers product experiences and deterministic tool outputs (numbers must be correct and auditable). • Implement data quality and validation workflows so failures are observable and fixable (quarantine/error surfacing instead of silent wrongness). • Make sound build vs buy recommendations for integration tooling and infrastructure (unified APIs, managed ETL, warehouses, observability, etc.). • Work directly with the founder and pilot partners to unblock access issues, validate data, and iterate quickly.