Quality & Reliability Engineer (AI Systems & Workflow Assurance)
Role details
Job location
Tech stack
Job description
We are seeking a Quality & Reliability Engineer to ensure the stability, correctness, and performance of AI-native healthcare workflows and operational systems across the Clinically AI platform.
This is not a traditional QA role focused only on manual testing or end-of-cycle validation. This role sits at the intersection of software reliability, workflow systems testing, AI behavior validation, and operational quality engineering in complex healthcare environments.
You will ensure that clinicians, administrators, compliance teams, and operational users can confidently rely on AI-powered systems in real-world healthcare workflows. This includes validating system correctness, identifying edge cases in workflow behavior, improving release confidence, and ensuring that AI-driven outputs behave reliably within operational constraints.
The ideal candidate thinks deeply about systems, workflows, edge cases, failure modes, and operational behavior, not just test cases. You should be excited by ensuring reliability in AI-native systems where outputs may be probabilistic, workflows are complex, and correctness directly impacts clinical operations.
This role will work closely with product, engineering, implementation, and leadership teams in a highly collaborative and fast-moving startup environment.
Requirements
Do you have experience in Quality assurance within IT?, * Systems Reliability Thinking You think beyond individual features and care deeply about how systems behave across workflows, services, and real-world usage conditions.
- AI-Native Testing Mindset You understand how AI changes software behavior, especially in non-deterministic systems where outputs require workflow-aware validation.
- Workflow Awareness You understand how clinicians, administrators, and operational teams interact with software in real environments under pressure.
- Strong Analytical Judgment You can isolate failures across frontend, backend, APIs, and distributed systems with clarity and precision.
- Collaboration & Communication You communicate effectively across engineering, product, and implementation teams.
- Startup Adaptability You are comfortable iterating quickly, working through ambiguity, and helping define structure in evolving systems.
REQUIRED
- 3+ years of experience in QA engineering, software testing, or reliability engineering roles in SaaS, web applications, or platform environments
- Experience building automated test suites using Playwright or similar frameworks (Cypress, Selenium, WebdriverIO, etc.)
- Strong understanding of frontend behavior, browser debugging tools, and network inspection
- Experience validating REST APIs, including authentication, permissions, and data integrity
- Proficiency in JavaScript or TypeScript for test automation
- Experience working within CI/CD pipelines (GitHub Actions preferred)
- Strong ability to identify root causes and communicate technical findings clearly
- Experience collaborating closely with engineering and product teams in agile environments
- High ownership mindset and ability to operate in fast-paced, evolving systems
PREFERRED BUT NOT REQUIRED (Nice to Have)
- Experience testing React applications, browser extensions, or real-time web systems
- Exposure to mobile testing environments (iOS/Android)
- Experience with performance or load testing tools (k6, JMeter, Locust)
- Familiarity with cloud infrastructure and distributed systems
- Experience working in regulated environments (HIPAA, SOC2, NIST)
- Experience testing AI/LLM-powered or non-deterministic systems
Benefits & conditions
This is a full-time hybrid role based in San Diego, CA, with onsite work required Monday through Thursday.
We offer competitive compensation, healthcare coverage, and flexible time off.
The interview process includes interviews focused on systems reliability thinking, testing strategy, AI behavior validation, and collaboration approach. Final candidates may complete an exercise focused on designing a reliability strategy for an AI-assisted healthcare workflow system.