Principal Software Engineer

Foundation Health is an AI operating system for pharmacy, built by a team with firsthand experience in the industry's challenges. They aim to use AI to streamline prior authorizations, reduce administrative work for pharmacists, and prevent prescriptions from being missed. The company is developing integrated infrastructure to improve care coordination, specifically for specialty pharmacy. Their vision is for pharmacy teams to focus on patient care, with AI handling routine tasks.

The company's platform is currently live in major US health systems, including Intermountain Health, where its AI handles patient communication via voice and SMS and integrates with Epic workflows. They have a public partnership with Amazon Pharmacy and have raised $20M in Series A funding led by Define Ventures. The team consists of approximately 70 people, has achieved product-market fit, and is experiencing rapid growth driven by enterprise customers.

This is the most senior individual contributor (IC) role in the US organization. The successful candidate will be responsible for architecture across multiple product areas, designing systems capable of supporting significant growth without requiring a complete rewrite. They will also be hands-on in solving the company's most challenging technical problems, prototyping solutions for complex issues. The role involves direct collaboration with the VP of Engineering and CEO, with a management style that emphasizes hands-on involvement at all levels.

Key technical areas of ownership include: - Agentic AI in a regulated environment: developing voice and SMS agents for patient interaction, ensuring safety through evaluations, guardrails, and pharmacist oversight. - Deep EHR integration: working with systems like Epic queues, prior authorization automation, and 340B compliance, where accuracy is critical. - Target-state architecture: designing and building the initial stages of platform migration to support scaling from a few health systems to hundreds. - AI engineering: focusing on model selection, context management, cost/latency trade-offs, and evaluation methodologies.