Medical AI Scientist (£95K – £115K + Equity) at government-authorized medical AI certification […]

Company: Jack & Jill
Apply for the Medical AI Scientist (£95K – £115K + Equity) at government-authorized medical AI certification […]
Location: London
Job Description:

Medical AI Scientist

Salary: £95K – £115K + Equity

Location: London, UK

Company Description: Government-authorized medical AI certification platform accelerating healthcare access

Job Description

You will serve as a critical technical authority evaluating the next generation of medical AI devices. By assessing model performance, datasets, and clinical evidence, you will ensure bleeding‑edge technologies reach patients safely. This role bridges the gap between innovative AI research and regulatory approval within an exponentially growing, revenue‑generating healthtech leader.

Why this role is remarkable

  • Direct impact on global healthcare by authorizing the market entry of groundbreaking medical AI systems for world‑class clients.
  • Join a high‑growth startup with proven product‑market fit, significant revenue traction, and a mission‑critical role in the healthcare ecosystem.
  • Work at the intersection of clinical medicine, advanced AI, and regulation alongside a multidisciplinary team of clinicians and expert engineers.

What You Will Do

  • Critically evaluate AI/ML performance testing methodologies and datasets to verify clinical benefit and performance claims.
  • Communicate complex statistical and machine learning concepts to manufacturers, internal teams, and non‑technical stakeholders.
  • Represent the organization at global conferences and monitor the latest healthcare AI research to drive internal system improvements.

The ideal candidate

  • Holds a PhD in Healthcare AI or a Master’s in Health Data Science with a strong focus on medical statistics.
  • Proven experience evaluating AI/ML model performance metrics and training datasets within regulated or clinical environments.
  • Expert ability to critically assess statistical analyses and articulate scientific positions on model validation and bias frameworks.

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Posted: May 24th, 2026