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.
Clera is building autonomous AI agents that handle end-to-end recruiting operations. The company's AI agents find candidates, write personalized outreach, handle conversations, qualify fit, and schedule interviews without human intervention. Clera has raised pre-seed funding from top investors and has grown to marketplace scale with 40,000+ talents and hundreds of top startup customers. The company is now scaling its AI-native recruiting platform by hiring operators who supervise the agents, handle high-judgment moments, and close candidates that require human touch.
