SDET: Architect AI-Driven Testing for Healthcare OS

Pantera Capital; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity from day one

Job summary

Ease Health is hiring a Software Development Engineer in Test (SDET) to build core automation frameworks for an AI-native healthcare platform. You will ensure reliability as the product scales, focusing on AI/NLP workflows, LLM outputs, and robust testability across clinical processes.

This is a highly technical, product-minded role where you’ll shape the testing strategy, own automation infrastructure, and partner with engineers to prevent failures in production.

Qualifications

• We’re looking for a strong engineer with a quality mindset and a focus on test architecture.
• Experience evaluating LLMs, prompt regression, and autonomous agent workflows is highly valued.
• No CS degree is required, but strong coding and systems thinking are essential.

Responsibilities

• Design, architect, and maintain scalable automated testing frameworks across the stack.
• Develop strategies for testing AI systems and non-deterministic components.
• Embed automation into CI/CD pipelines to enable rapid, confident releases.
• Partner with product and engineers to build testability into features from start.
• Identify edge cases, race conditions, and compliance gaps in complex systems.
• Help define testing philosophy and infrastructure for the company.

Skills

Test architecture

Automation

AI/LLM testing

CI/CD integration

Ownership mindset

Tools

CI/CD tooling

Testing frameworks

Job description

Ease Health is hiring a Software Development Engineer in Test (SDET) to build core automation frameworks for an AI-native healthcare platform. You will ensure reliability as the product scales, focusing on AI/NLP workflows, LLM outputs, and robust testability across clinical processes.

This is a highly technical, product-minded role where you’ll shape the testing strategy, own automation infrastructure, and partner with engineers to prevent failures in production.