Role Overview:
We are looking for a Senior Product Manager to own the intelligence layer of our platform — the AI models, pipelines, and systems that power personalized recommendations and diagnostics. You will work at the intersection of machine learning engineering, clinical science, and product design, shaping how AI becomes a trusted part of our users' health journey.
This is a high-ownership, high-impact role for someone who thrives in technically complex environments and is energized by the challenge of making AI both rigorous and human.
Responsibilities:
- AI product strategy — define what the intelligence layer does, what signals it ingests, and how outputs are surfaced to users and clinical stakeholders
- Model evaluation framework — build eval suites, define quality metrics, set safety guardrails, and drive continuous improvement of model performance
- Clinical data loop — work with domain experts to translate specialized knowledge into structured training data requirements; manage the feedback cycle between experts and models
- Architecture decisions — collaborate with ML engineers on retrieval design, knowledge-base curation, and inference quality standards
- Regulatory alignment — partner with Legal & Compliance to ensure AI outputs meet applicable health and data protection standards
- Roadmap ownership — prioritize and sequence AI product work against hard delivery milestones, balancing speed with safety
Requirements:
- 5+ years in product management; at least 2 years with an AI/ML product as your primary responsibility.
- Shipped AI features end-to-end: data pipeline → model training → inference → user-facing output.
- Comfortable reading ML experiment results and making product tradeoffs with engineers directly, without a technical translator.
- Hands-on experience with RAG systems, LLM fine-tuning, or knowledge-retrieval in a production context.
- Track record of working with domain experts (clinicians, scientists, or equivalent) to convert expert knowledge into model training artefacts.
- Strong grounding in AI safety, responsible AI principles, and evaluation methodology.
Nice to Have:
- Background in health, biotech, medtech, or life sciences.
- Familiarity with EU AI Act, health software regulations, or GDPR implications for AI systems.
- Experience building personalization or adaptive recommendation engines.
