About Clarion

Clarion is building the most advanced AI communication layer for healthcare. Our AI agents handle the overwhelming volume of calls and messages that clinics face daily—from scheduling to billing to prescription refills—freeing staff to focus on patient care. We're an in-person company based in New York.

Today, clinics miss 30-40% of inbound calls due to staffing shortages. We're solving this by automating complex workflows end-to-end, already serving tens of thousands of patients monthly across virtual care companies, health systems, and a $5B health insurance company.

We've raised $5.4M from Accel, Y Combinator, Sequoia (scout), and healthcare founders from Ophelia, Medallion, and Counsel Health.

Our founders bring both clinical and technical expertise—our CEO Ryan is a Stanford/Harvard-trained physician who was on the founding teams of Two Chairs and Ophelia, while our CTO Jeffrey built voice AI at Amazon Alexa and led AI/ML teams at Salesforce.

Join us as we transform healthcare communication—creating a future where patients get faster resolutions and providers reclaim time for what matters most.

Why Join Us?

• Early-stage impact with proven traction: Dozens of paying customers, rapidly growing revenue, and strong market signals—all in the high-impact environment of an early-stage startup.

• Tackling a massive healthcare challenge: Address a critical problem in communication that affects millions of patients and providers daily, using technology that truly transforms the patient experience.

• Technical founding team: Collaborate directly with experienced founders who understand healthcare operations and technical development, enabling faster decisions and better outcomes.

• Cutting-edge tech frontier: Harness the latest generative AI models—LLMs, voice synthesis, and real-time transcription—to build conversational agents that push healthcare innovation beyond current limits.

• In-person collaboration advantage: We've prioritized in-person work from day one for faster progress, stronger team bonds, and a cohesive culture.

Responsibilities

• Design, develop, and iterate on AI agents for healthcare communication, focusing on safety, reliability, and performance.

• Optimize LLMs through prompt engineering, tool calling, and advanced techniques like RAG and MCP.

• Implement and maintain LLM observability systems to monitor, debug, and analyze agent behavior in production.

• Build conversational systems, integrating voice capabilities where needed, and collaborate on ethical AI deployments.

• Contribute to the product roadmap by identifying AI improvements from real-world healthcare data.

Requirements

• Proven AI agent experience: 2+ years developing AI agents, with emphasis on safety and reliability in production.

• Expertise in AI techniques: Skilled in prompt engineering, tool calling, MCP development, and retrieval-augmented generation (RAG) to enhance agent accuracy and performance.

• LLM observability proficiency: Strong in monitoring, debugging, logging, and performance tools for transparency and traceability.

• Conversational systems background: Hands-on experience with chat systems; voice systems experience is a plus.

Interesting Technical Challenges

• Healthcare-specific LLM tuning: Fine-tune and prompt-engineer LLMs to handle medical terminology, clinical contexts, and empathetic responses while mitigating biases and hallucinations.

• AI-driven workflow orchestration: Design agents using tool calling, agentic workflows, and RAG to manage multi-step processes like scheduling, insurance verification, prescription management, and documentation.

• LLM observability implementation: Build frameworks for real-time monitoring, logging, and traceability to detect issues in healthcare AI agents.

• Deploying generative AI in healthcare: Lead rollouts of LLM-based voice agents, ensuring integration, ethical compliance, HIPAA adherence, and scalable safety.

Interview Process

At each stage, we decide within 24 hours and update you shortly after:

• First Chat (30 min) [Virtual]: Discuss your relevant experience and technical strengths.

• System Design Screen (1 hr) [Virtual]: Exercise focused on designing LLM-based applications.

• Applied Coding Exercise (1 hr) [Virtual]: Hands-on challenge for building and optimizing AI agents in healthcare.

• Onsite (Half-day) [In-Person]: Visit our New York office to meet the team, explore our platform, and solve technical problems collaboratively.