Founding Member of Technical Staff

Role Overview As a Founding Member of Technical Staff, you'll help build the reasoning methodology for safety-critical AI validation. You’ll investigate how models behave, where validation breaks down, and how claims about model behavior can be supported, challenged, or refined with evidence. Key Responsibilities Design and execute investigations into how models perform and fail. 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 evidence that supports, challenges, or refines claims about model performance and safety. Develop Tessel’s evidence methodology. Define how claims, arguments, and evidence should be structured for rigorous AI validation. Pressure-test assumptions, critique weak argument structures, and turn repeated investigations into reusable methods, workflows, and platform primitives. Contribute to shipping the platform by writing production-quality Python, building agentic workflows for evidence investigation, and prototyping front-end features with AI tooling. This is a founding role. We're looking for someone who can challenge our assumptions, contribute beyond their immediate technical domain, and help build foundational infrastructure for the future of safety-critical AI. Skills & Experience Required: Degree in CS, math, physics, engineering, or a related quantitative field, or equivalent demonstrated depth. Strong ML, statistics, and data science fundamentals, with an ability to understand the math behind methods, recognize broken mathematical assumptions, and reason about trade-offs. Expert Python skills, from raw data analysis to writing platform code others will use. Strong engineering judgment on code structuring, interface boundaries, and trade-offs related to reusability. Comfort using AI tooling as a primary mode of working. Nice-to-Have: 3–5 years of professional ML experience or a PhD in model evaluation, robustness, OOD detection, interpretability, or a related area. Experience with AI/ML medical device submissions, FDA review processes, or other regulated environments. Background in safety case methodology in aviation, automotive, healthcare, or other safety-critical fields.