About the role
Responsibilities:
- Launch and scale high-quality AI agents.
- Build and deploy voice AI for automating patient activation and outbound calls to insurance, vendors, and providers.
- Design individual agents that orchestrate end-to-end workflows such as DME delivery, prior authorisations, and care coordination.
- Build copilots that assist and nudge advocates in real time.
- Build the eval and reinforcement learning pipeline.
- Set up the evaluation infrastructure that measures agent quality across every interaction.
- Capture the right data from product usage, build the feedback loop, and implement RL pipelines so that agent performance improves continuously with real-world usage.
- Create the usage-driven product improvement flywheel.
- Scale AI Nurse by composing multiple agents.
- Bring together voice, workflow, and copilot agents into a unified AI Nurse experience.
- Ensure that as we scale, each agent reinforces the others through shared learning and a consistent improvement loop.
- Ship fast and iterate with real users.
- Deploy to production, monitor how agents perform with actual patients, and improve based on real conversations.
- Own the tight loop from usage data to system-level improvements.
- Shape the technical roadmap.
- Work with the founders to decide what to build next.
- Bring deep knowledge of what's possible with current AI capabilities and help us make smart bets on where the technology is heading.
- Lay the foundation for scale.
- Make architectural decisions that will hold up as we grow from hundreds of patients to hundreds of thousands.
- Document systems, establish best practices, and build with the next engineer in mind.
Requirements:
- You've built AI agents or LLM-powered systems from scratch and shipped them to production, not just demos, but systems handling real interactions with real users at scale.
- You have hands-on experience building evaluation pipelines for AI systems, designing metrics, capturing signals from production usage, and using that data to systematically improve agent quality.
- You have experience with reinforcement learning from human feedback (RLHF), reward modelling, or other feedback-driven improvement loops for LLM-based systems.
- You've monitored and debugged AI systems in production, you know what it takes to keep agents reliable when they're interacting with real people.
- You're comfortable with ambiguity.
- The playbook doesn't exist yet, and you're excited to build it.
Nice to Have:
- Experience in healthcare, health tech, or regulated industries (HIPAA, PHI handling).
- Familiarity with Medicare, insurance workflows, or clinical operations.
- Experience with voice AI systems, speech-to-text, text-to-speech, and real-time voice orchestration.
- Background in RAG systems, vector databases, and knowledge retrieval pipelines.
- Contributions to open-source AI projects or a portfolio of side projects that show your range.