About Clarion

At Clarion, we’re rebuilding how healthcare communicates in the age of AI.

Today, clinics miss 30–40% of patient calls, while staff are overwhelmed by administrative work. We believe AI agents should handle these workflows—scheduling, billing, prescription refills, and follow-ups—so healthcare teams can focus on actual patient care.

We’re building the communication infrastructure modern healthcare needs. Our AI agents don’t just answer calls—they complete workflows end-to-end, ensuring patients never go unheard. To date, we’ve handled hundreds of thousands of patient interactions across virtual care companies, health systems, and a $5B health insurance company.

Clarion was founded by a Stanford/Harvard-trained physician (founding team at Two Chairs and Ophelia) and an ex-Amazon Alexa engineer who led AI/ML teams at Salesforce. We’ve raised $10M from Accel, Y Combinator, Sequoia (Scout), and leading healthcare founders. We’re an in-person team in New York, moving fast to solve one of healthcare’s most critical problems.

Why This Role Is Special

• Real traction, early-stage impact: Dozens of paying customers, rapidly growing revenue, and the opportunity to own foundational backend systems at a critical inflection point.

• Mission-critical problem: Healthcare communication failures affect millions daily—your work directly powers systems patients and providers rely on.

• Elite technical context: Work closely with founders who deeply understand both healthcare operations and large-scale AI systems, enabling fast decisions and real ownership.

What You’ll Own

• Core backend systems: Design, build, and own the services, data models, and business logic powering Clarion’s AI agents and healthcare workflows.

• Workflow orchestration: Build infrastructure for multi-step, asynchronous workflows—including agentic AI workflows with conditional logic, retries, and graceful failure handling.

• Healthcare integrations: Own high-stakes integrations with EHRs and legacy healthcare platforms via APIs and RPA.

• Platform foundations: Create internal abstractions and tooling that allow the team to deploy and customize AI assistants for new customers quickly and reliably.

• Security and compliance: Implement and maintain backend authentication, authorization, and HIPAA-compliant architecture required for enterprise healthcare.

Interesting Technical Challenges

• Generative AI at Scale: Design and operate production systems using LLMs with real-time monitoring, safety controls, and enterprise reliability.

• Agentic workflows: Architect backend systems that coordinate AI agents across complex healthcare workflows with strong guarantees.

• Enterprise reliability: Build systems supporting thousands of concurrent patient interactions with high uptime and fast incident detection.

• Fragmented healthcare systems: Design resilient abstractions across modern APIs and legacy EHRs while maintaining security and compliance.

What We’re Looking For

• 5+ years of experience designing, building, and scaling backend systems in high-reliability production environments.

• Strong backend expertise across APIs, relational databases, async workflows, and distributed systems.

• High ownership and agency—you thrive in ambiguity and take responsibility for system outcomes.

• Ability to communicate clearly, collaborate cross-functionally, and surface risks early.

• Bonus: experience with security, auth, or regulated environments (HIPAA familiarity is a plus).

You’ll be a great fit if you enjoy owning critical backend systems end-to-end, solving complex problems under real-world constraints, and building infrastructure that directly impacts patient care.

Interview Process

We move quickly and communicate clearly at every stage:

• Intro Chat (30 min, Virtual): Background, role context, and mutual fit.

• Technical Deep Dive (1 hr, Virtual): Backend systems and architecture with the CTO and a senior engineer.

• Onsite Team Day (Half Day, NYC): Collaborate on real problems, present a past project, meet the team, and assess mutual fit.

Decisions are typically made within 24 hours of each stage.