Pulmonary Physician

Turing•San Francisco, CA
•Remote

About The Position

In this project, the Pulmonary Physician will apply expertise in pulmonary and critical care medicine to fine-tune large language models. The role focuses on ensuring AI systems provide accurate, empathetic, and safe medical reasoning in respiratory diagnostics, ventilator management, and ICU-level decision-making. The position involves developing and solving advanced clinical scenarios, reviewing AI-generated answers, identifying gaps in model knowledge, and collaborating with AI researchers and medical peers. The engagement is fully remote and lasts one month, with a possible extension. The expected commitment is 20–40 hours per week, at least four hours per day, with some overlap in Pacific Time. This is a contractor/freelancer engagement with no medical or paid leave.

Requirements

  • MD or equivalent.
  • Active clinical practice within the last 3+ years.
  • Board certification or equivalent qualification in Pulmonary Medicine and/or Critical Care Medicine.
  • Strong knowledge of pulmonary and critical care medicine, including respiratory failure, mechanical ventilation, lung diseases, and ICU management.
  • Ability to explain complex medical reasoning in clear, patient-friendly language.
  • Excellent written and spoken English.

Nice To Haves

  • Familiarity with digital medical tools.
  • Education from a top-tier institution, such as Harvard, Stanford, UCSF, or Oxford.

Responsibilities

  • Design and solve advanced clinical scenarios in pulmonary and critical care medicine, including ventilator management, ARDS, COPD exacerbations, and sepsis-driven respiratory failure.
  • Evaluate AI-generated responses for diagnostic accuracy, clinical judgment, and evidence-based reasoning in pulmonary and critical care medicine.
  • Identify gaps in AI understanding across pulmonology and intensive care, including interstitial lung disease, pulmonary hypertension, and ICU protocols.
  • 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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