Founding Member of Technical Staff

About the Role This is a founding engineering role at an early-stage healthtech / safety-critical AI startup building evidence infrastructure for AI model validation in medical diagnostics. As a Founding Member of Technical Staff, you will help shape the core reasoning methodology that underpins how AI safety claims are investigated, structured, and validated — across the full product lifecycle. You'll work directly alongside the founding team, contributing across technical domains and helping lay the infrastructure for the future of safety-critical AI. What You'll Do Design and execute investigations into how AI models perform and fail across real-world scenarios. Analyze input data, model outputs, and internal representations to evaluate data quality, generalization limits, distribution shift, subgroup performance, and demographic bias. Surface failure modes and produce structured evidence that supports, challenges, or refines claims about model performance and safety. Develop and evolve the company's evidence methodology — defining how claims, arguments, and evidence should be structured for rigorous AI validation. Pressure-test assumptions, critique weak argument structures, and systematize repeated investigations into reusable methods, workflows, and platform primitives. Write production-quality Python, build agentic workflows for evidence investigation, and prototype front-end features using AI tooling. Contribute beyond your immediate technical domain — this is a founding role that requires ownership, versatility, and the willingness to challenge assumptions. What We're Looking For Required: Degree in CS, mathematics, physics, engineering, or a related quantitative field — or equivalent demonstrated depth. Strong ML, statistics, and data science fundamentals; ability to understand the math behind methods, identify broken assumptions, and reason about trade-offs. Expert Python skills, spanning raw data analysis through to platform-level code others will rely o