Founding Member of Technical Staff

Clera•San Francisco, CA

7d•$150,000 - $200,000•Onsite

About The Position

This is a founding technical role at a seed-stage startup building evidence infrastructure for safety-critical AI — with an initial focus on medical diagnostics. You'll help shape the reasoning methodology that underpins rigorous AI validation: investigating how models behave, where validation breaks down, and how claims about model behavior can be supported, challenged, or refined with evidence. As one of the earliest team members, you'll operate with high ownership, contribute across disciplines, and help lay the foundations for a platform that AI vendors and healthcare institutions will rely on throughout the full product lifecycle. No visa sponsorship is available. Candidates must be authorized to work in the United States and able to work on-site in Sunnyvale, CA.

Requirements

Degree in CS, mathematics, physics, engineering, or a related quantitative field — or equivalent demonstrated depth.
Strong ML, statistics, and data science fundamentals; able to understand the math behind methods, recognize broken assumptions, and reason about trade-offs.
Expert Python skills, spanning raw data analysis through platform-grade code.
Strong engineering judgment around code structure, interface boundaries, and reusability trade-offs.
Comfort using AI tooling as a primary mode of working.
Demonstrated ability to operate as a founding team member — taking end-to-end ownership of features and contributing beyond a core technical domain.
Strong cross-functional communication skills; able to present evidence, claims, and validation results to both technical and non-technical audiences.

Nice To Haves

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 from aviation, automotive, healthcare, or other safety-critical fields.

Responsibilities

Design and execute investigations into how 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 evidence that supports, challenges, or refines claims about model performance and safety.
Develop 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 convert 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.
Communicate validation results clearly to both technical and non-technical stakeholders.