CDSS Doctor
AI Summary
Design and refine clinical agents within a Clinical Decision Support System, build evaluation benchmarks using real-world cases, and stress-test agents with realistic clinical scenarios to improve AI healthcare products.
About this role
Why Telepatia
The world's most scarce resource isn't time — it's medical attention. Doctors spend 40–70% of their day typing instead of caring for patients. At Telepatia, we're changing that.
We build real-time AI products for healthcare — medical transcription, clinical record generation, and EMR integrations across Brazil and Colombia. Our flagship, AI Doctor, is part of a suite of four AI Healthcare Employees built by doctors, for doctors.
🩺 What You'll Do
Design and refine clinical agents within the CDSS: define purpose blocks, behavioral scope, and edge cases based on real patient encounters (medications, contraindications, inappropriateness, preventive/predictive care).
Build and maintain evaluation benchmarks using anonymized real-world cases: generate Brazilian clinical transcripts, define expected vs. unexpected alerts, and iterate on false positives and false negatives.
Stress-test agents with realistic clinical scenarios, identifying edge cases and behavior under clinical ambiguity.
Contribute to the CDSS product vision: propose new iterations, identify uncovered clinical gaps, and help define the system’s evolution.
Work closely with product, design, and engineering teams: translate clinical reality into agent requirements and provide pushback when solutions are not clinically sound.
Support clinical-facing relationships with client institutions: contribute to technical differentiation, validate alerts, and analyze acceptance/rejection patterns.
Contribute to the clinical publication pipeline (CDSS performance, alert response patterns, multilingual deployment across LATAM).
🩺 What You Have
General Medicine doctor (MD)
Strong clinical reasoning skills, with solid diagnostic thinking and comfort navigating medical guidelines and evidence.
Exceptional attention to detail: able to spot inconsistencies in prompts, transcripts, or guidelines that others might miss.
An iterative, data-driven mindset: comfortable working with metrics (acceptance rates, rejection rates, pending alerts) and continuously improving based on real-world signals.
Clear written and verbal communication: structured thinking, with the ability to provide respectful but firm pushback.
Strong ownership mindset: focused on driving outcomes (not just tasks) in fast-moving, ambiguous environments.
Fluent in English.
Skills
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