At Claris, an Apple company, we're transforming how knowledge workers interact with applications-shifting from static, deterministic workflows to adaptive, intelligent systems powered by innovative Artificial Intelligence. We're not just adding AI features; we're building a trustworthy AI platform from the ground up, where security, privacy, and reliability are foundational principles inspired by Apple's unwavering commitment to user protection.
Requirements
- 3 to 5+ years of hands-on experience with production-level backend QA, with expertise in testing scalable, fault-tolerant SaaS applications and microservices.
- Strong experience with Go or Python programming languages and testing tools/frameworks (e.g., Ginkgo, Pytest).
- Demonstrated ability to build clear, comprehensive test scenarios and systematic testing strategies for complex distributed systems.
- Strong understanding of RESTful API design, microservices architecture, and testing modern, scalable backend systems.
- Foundational knowledge of LLM/ AI concepts and hands-on exposure to testing AI-powered features, prompt engineering, or LLM API integration in a production environment.
- Experience with testing solutions using WebSockets and webhooks.
- Familiar with containerization (Docker, Kubernetes) and cloud environments (AWS or GCP).
Responsibilities
- architecting comprehensive testing and evaluation strategies for our backend platform-with a growing focus on AI-powered workflows.
- designing frameworks that validate complete flows from user request through service execution to final outcome, with special attention to AI-specific concerns.
- develop systematic testing approaches for both deterministic microservices and non-deterministic AI models.
- build observability systems that ensure behavior remains trustworthy in production.
- transform innovative AI research into reliable, production-ready solutions that organizations depend on, while maintaining the rigorous engineering rigor that makes our platform trustworthy.
- apply proven QA rigor to the emerging challenge of AI validation.
Other
- technically excellent, strategic problem solver who brings deep backend QA expertise combined with genuine curiosity about AI.
- The ideal candidate combines years of production quality assurance experience with foundational AI knowledge, strong communication skills, and ability to thrive in cross-functional collaboration.
- You understand that great QA isn't just finding bugs-it's building confidence that systems work reliably at scale.
- You're eager to apply proven QA rigor to the emerging challenge of AI validation.
