About the teamWhat makes health systems unique from other verticals adopting agents is the use of repetitive intelligence: the same closed-ended task is performed tens to hundreds of thousands of times per health system per year.
The team manages the agent lifecycle for closed-ended tasks that demand repetitive intelligence, from learning about a customer pain point to launching an agent that runs on every patient.
About the role- Inform optimal agent design through experimentation
- Optimize agentic correctness, cost, and velocity
- Increase agent developer velocity
- Establish reliable connectors with third-party systems
- Own agent testing and evaluation
- Identify and advocate for platform improvements
- Identify opportunities that demand repetitive intelligence
Your background- Strong technical foundation: comfortable writing code, collaborating in technical teams, and understanding core agent concepts such as delegation, memories, tool-calling, compaction, structured outputs, context engineering, and prompt design
- Strong scientific background: comfortable running experiments and with AI fundamentals such as dataset curation, training an AI system, designing evaluations and metrics, error analysis, and drawing sound conclusions from noisy results
Even better- Experience building AI systems or agentic systems
- A CS, engineering, or math degree or equivalent technical experience
- Experience with healthcare data or systems, such as EHRs, clinical documentation, or HL7/FHIR
- Experience taking an AI system from prototype to production at scale
- Published research or open-source contributions in machine learning or AI agents
