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 on. * Strong engineering judgment on code structure, interface boundaries, and reusability trade-offs. * Demonstrated ability to operate as a founding-team-caliber contributor — taking end-to-end ownership and contributing across domains. * Strong cross-functional communication skills; able to present evidence, claims, and validation results to both technical and non-technical stakeholders. * Comfort using AI tooling as a primary mode of working. * Authorized to work in the United States without visa sponsorship; able to work on-site in Sunnyvale, CA. ### Nice to Have: * 3–5 years of professional ML experience, or a PhD in model evaluation, robustness, out-of-distribution detection, interpretability, or a related area. * Experience with AI/ML medical device submissions, FDA review processes, or other regulated environments (e.g., FDA 510(k), De Novo, EU AI Act). * Background in safety case methodology in aviation, automotive, healthcare, or other safety-critical fields. ### Compensation & Benefits * **Salary:** $150,000 – $200,000 USD annually * Founding team equity and early-stage upside ### Location On-site in **Sunnyvale, CA, United States**. This is a full-time, in-office role. Visa sponsorship is **not available** — candidates must be authorized to work in the US without sponsorship. Originally posted on [Himalayas](https://himalayas.app)