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Agent Researcher

BunkerhillhealthSF + NYC🇺🇸United States

Quick overview

SeniorityMid Senior
Employment typeFull Time
Work modeHybrid
LocationSF + NYC, United States
Machine Learning

Job description

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

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