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Bunkerhillhealth

Agent Researcher

SF + NYC · On-site · FullTime · Engineering

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About the role

Machine LearningA/B Testing
About Bunkerhill Bunkerhill is a long-horizon agent for health system knowledge work. Health systems use our agent’s built-in capabilities for work that is performed consistently across customers, and use its tailored capabilities for work that is specific to them. We are an in-person, in-office team in SF and NYC that exemplifies three core values: build for the patient, own it ship it, be bold together. About the team What 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