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AI Surgical Outcome Prediction Developer

Paisley Suzuki

Full-time · Job · Chicago

About the role

A surgeon deciding whether to recommend a complex procedure needs to know the patient-specific complication risk — not the population average from a textbook published in 2015. Our surgical outcome prediction platform processes preoperative data across 200+ clinical variables to estimate individual patient risk for 14 procedure types, helping surgical teams make better decisions and reducing preventable complications. We've trained on outcomes data from 120,000 procedures across 18 hospital systems. The model achieves 0.82 AUC on held-out test sets. The gap between 0.82 and clinical deployment confidence is partly technical — better calibration, more robust uncertainty quantification — and partly validation: surgeons won't trust a model they cannot interrogate. You'll own model improvement and clinical validation. That means improving calibration, building the explanation layer that shows surgeons which patient factors drove the risk estimate, and designing the prospective validation protocol that will generate the evidence clinicians need to trust the system.

Responsibilities

  • Improve surgical outcome model calibration and uncertainty quantification
  • Build patient-level explanation features showing which variables drove risk estimates
  • Design prospective validation protocols suitable for clinical trust-building
  • Develop models for two new procedure types currently in the pipeline
  • Collaborate with clinical partners on model integration into surgical workflow tools
  • Document model methodology for IRB review and regulatory submission support

Requirements

  • 5+ years in ML engineering with clinical or healthcare domain experience
  • Deep expertise in model calibration, reliability, and clinical validation methodology
  • Strong Python skills with scikit-learn and PyTorch experience
  • Understanding of medical ethics, IRB processes, and clinical deployment considerations
  • Ability to communicate model behavior to clinical practitioners

Benefits

  • Fully remote
  • Health, dental, and vision
  • Equity package
  • $4,000 annual conference and learning budget
  • Generous parental leave

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics scikit-learn pandas

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Level

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Language

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Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics scikit-learn pandas sql

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