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.
We're looking for an experienced backend QA engineer who combines deep expertise in traditional quality assurance with hands-on knowledge of AI/LLM systems. This is an opportunity to shape how enterprise AI is validated and evolved, while bringing rigorous testing rigor to emerging technology. If you're passionate about testing scalable cloud-based applications, understand both API-driven microservices and AI workflows, and are excited by the challenge of validating next-generation intelligent systems, this is your team.
Description
We are seeking an experienced QA Engineer to join our backend team at the intersection of high-performance infrastructure and AI validation. You'll work with a Go-based microservices architecture optimized for performance, paired with a Python AI service layer using large language models.
Your core mission is architecting comprehensive testing and evaluation strategies for our backend platform—with a growing focus on AI-powered workflows. This means designing frameworks that validate complete flows from user request through service execution to final outcome, with special attention to AI-specific concerns. You'll develop systematic testing approaches for both deterministic microservices and non-deterministic AI models. You'll build observability systems that ensure behavior remains trustworthy in production.
You'll need deep understanding of Cloud concepts, scalable microservice architecture, and modern testing practices. Equally important is understanding how AI integration impacts quality assurance—how to evaluate LLM outputs, design guardrails, and validate that AI recommendations integrate cleanly into backend workflows. You'll transform innovative AI research into reliable, production-ready solutions that organizations depend on, while maintaining the rigorous engineering rigor that makes our platform trustworthy.
We are looking for a 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.
Minimum Qualifications
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.
Preferred Qualifications
Experience with testing solutions using WebSockets and webhooks. Familiar with OAuth and Single-Sign-On authentication.
Familiar with containerization (Docker, Kubernetes) and cloud environments (AWS or GCP).
Practical experience with adversarial testing, security validation, or evaluating LLM outputs for safety and quality concerns.
Knowledge of LangChain, LangGraph, or other AI frameworks, and observability platforms for monitoring AI system behavior.
Familiarity with implementing guardrails, safety constraints, or quality evaluation frameworks for AI systems.
Experience with NoSQL databases is desired.
Eeo Content
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