Ready to define the future of AI safety?

Ever wondered what it takes to truly trust AI in our most critical systems, especially when lives are on the line? At Clera, we're building the foundational infrastructure to make that trust a reality. We're a nimble, early-stage healthtech startup, pioneering how AI models are rigorously validated in medical diagnostics. As a Founding Member of Technical Staff, you won't just be coding; you'll be at the very heart of defining what "safe AI" truly means, shaping the methods we use to investigate, structure, and validate AI safety claims throughout a product's entire journey. Imagine working hand-in-hand with our founding team, laying the groundwork for how safety-critical AI systems are built and trusted worldwide. This isn't just a job; it's an opportunity to build the future of responsible AI from the ground up.

Overview

We're searching for a visionary engineer to join us as a Founding Member of Technical Staff, a fully remote, worldwide opportunity. This is a pivotal, hands-on role where you will play a crucial part in developing our core reasoning methodology. Your work will directly underpin how we investigate, structure, and validate AI safety claims for medical diagnostics, from initial concept through to deployment and beyond. You will contribute across diverse technical domains, helping to establish the essential infrastructure for a future where safety-critical AI is both powerful and demonstrably trustworthy.

Key Responsibilities

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 Clera's evidence methodology, defining how claims, arguments, and evidence should be structured for rigorous AI validation.
Pressure-test assumptions and critically evaluate potential weaknesses in AI models and validation processes.

Requirements

Strong foundational understanding of machine learning principles, model evaluation, and common failure modes.
Demonstrable experience in data analysis, statistical methods, and interpreting complex datasets.
Prior experience or a deep interest in safety-critical systems, healthtech, or medical diagnostics.
Exceptional analytical and problem-solving skills, with a meticulous attention to detail.
Ability to work independently, drive projects from conception to completion, and thrive in an early-stage startup environment.
Excellent communication skills, capable of articulating complex technical findings to diverse audiences.
Proficiency in relevant programming languages (e.g., Python) and data manipulation libraries.

What You'll Gain

The unique opportunity to be a foundational engineer at an early-stage startup poised for significant impact.
Direct collaboration with our founding team, influencing product direction and company culture from day one.
A chance to define the standards and methodologies for AI safety and validation in a critical industry.
Exposure to a wide range of technical challenges, fostering rapid professional growth and skill development.
A truly global impact, working on systems that will enhance trust and safety in medical AI worldwide.
Competitive compensation and equity, alongside a comprehensive benefits package.