Cardiologist

Turing•San Francisco, CA
•Remote

About The Position

In this project, the Cardiologist will apply expertise in cardiovascular medicine to fine-tune large language models. The role involves helping AI systems provide accurate, empathetic, and safe medical reasoning across cardiovascular diagnostics, interventional procedures, and long-term cardiac care. The work includes developing and solving advanced clinical scenarios, evaluating AI-generated responses, identifying gaps in model understanding across cardiology subspecialties, and collaborating with AI researchers and medical peers. The Cardiologist will provide structured expert feedback to support AI systems for clinical and educational applications. This is a fully remote contractor/freelancer engagement lasting one month, with a possible extension. The commitment is 20–40 hours per week, with a minimum of four hours per day and some overlap with Pacific Time. The role offers no medical or paid leave.

Requirements

  • MD or equivalent.
  • Active practice within the last 3+ years.
  • Board certification or equivalent qualification in Cardiology.
  • Strong knowledge of cardiovascular medicine, including coronary artery disease, heart failure, valvular disease, arrhythmias, and cardiac imaging.
  • Ability to explain complex medical reasoning in clear, patient-friendly language.
  • Excellent written and spoken English.
  • Education from a top-tier institution, with Harvard, Stanford, UCSF, and Oxford given as examples.

Nice To Haves

  • Familiarity with digital medical tools.

Responsibilities

  • Design and solve advanced clinical scenarios reflecting cardiology practice, including complex ECG and imaging interpretation and management of heart failure and arrhythmias.
  • Evaluate AI-generated responses for diagnostic accuracy, clinical judgment, and evidence-based reasoning in cardiovascular medicine.
  • Identify gaps in AI understanding across interventional cardiology, electrophysiology, and preventive cardiology.
  • Collaborate with AI researchers and medical peers to improve model quality and reliability.
  • Provide structured expert feedback to inform AI development for clinical and educational applications.
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