Senior Clinical Product Lead | 100% Remote within Europe

Docplanner is a global healthcare platform connecting more than 300,000 doctors with approximately 100 million patients every month across 13 markets. Noa is an AI-driven product within Docplanner aimed at helping people live longer, healthier lives by assisting doctors in administrative tasks, decision-making, and patient navigation through personalized medical histories.

• Medical degree and meaningful experience practising medicine with solid understanding of clinicians' day-to-day work.
• Experience in building and shipping digital health, clinical software, or AI products within product teams or entrepreneurial contexts.
• Ability to translate clinical problems into product decisions.
• Strong clinical judgment to convert quality and safety into technical standards.
• Ability to gain trust quickly from doctors and communicate clearly across clinical, product, technical, regulatory, and leadership teams.
• Comfort working hands-on at a senior level in a fast-moving, international team.
• Active use and understanding of generative AI, with plus for hands-on experience in prompting, evaluation, error analysis, human oversight, or AI-assisted development tools like Cursor and Claude Code.

Nice to have:

• Experience with ambient documentation, clinical templates, medical-knowledge systems, clinical coding, evaluation datasets or building/certifying regulated Software as a Medical Device (SaMD).

Shape what we build:

• Bring how doctors really work (habits, preferences, workflow, admin) into the product.
• Talk to doctors, doctor-to-doctor, and turn candid feedback into product decisions.
• Partner with Product on the roadmap: what to build and what will actually get used.

Make sure it's clinically sound:

• Define the clinical quality bar and intended outcomes for our AI products.
• Review and improve prompts, model outputs, clinical templates, workflows and terminology.
• Spot clinical failure modes: what good and unacceptable outputs look like.
• Direct clinical evaluation: decide what to test, in what mix (specialty, population, complexity and edge cases), and set the bar. Bootstrap this with a dedicated owner for process, tooling, and annotation guidelines.
• Turn model errors and clinician feedback into concrete product and model improvements.

Keep it safe and scalable:

• Work with ML/LLM teams, Regulatory experts, and the PRRC to keep intent, evidence, risks and controls aligned, including EU MDR-related and similar requirements.
• Build feedback loops with practising clinicians and our consultant-doctor network.
• Define how clinical-product expertise scales across future products, teams and markets.