Jobs at K Health

Design, build, and scale backend services and APIs to support AI agent execution and hybrid LLM inference stacks. Integrate with clinical systems (FHIR/EMR), implement observability, background workers, and event-driven architectures, and collaborate with data science and ML teams to deploy reliable, production-ready AI features.

Lead AppSec across the SDLC: design and implement application security controls, perform hands-on testing of web apps/APIs/cloud services, integrate automated security tooling into CI/CD, reduce developer friction, review architecture and code, support vulnerability management and incident response, evaluate third-party risks, and ensure healthcare regulatory compliance (e.g., HIPAA, GDPR).

Lead provider operations teams managing licensing, credentialing, onboarding, workforce management, scheduling, and provider support. Build scalable processes, define KPIs and dashboards, develop managers, and partner cross-functionally to launch markets and ensure provider readiness from offer acceptance through first patient.

Lead People strategy as an executive partner to scale corporate and clinical teams from ~80 to 200+ providers. Build clinician career architectures, oversee talent lifecycle (TA, onboarding, performance, rewards), implement people analytics and HR systems, drive culture, inclusion, retention, and organizational design during high-growth expansion.

The Senior Data Engineer will design and optimize data services and pipelines, manage data quality, and collaborate with cross-functional teams to enhance data-driven decision-making.

Lead design and delivery of reliable, privacy-first full‑stack systems for clinical AI products. Architect reusable frameworks, own end-to-end features (clients to Kotlin/JVM backends), enforce performance and security standards, mentor engineers, and modernize platform tooling including agentic AI developer tools.

Own clinician-facing products that surface AI-gathered patient context, support decision-making, and reduce documentation burden. Partner with engineering, ML, clinical, and design teams to build explainability, review/override workflows, and measurable workload reductions. Validate workflows with practicing physicians and lead safety and escalation protocols with partners.