About the JobFriendli Suite is our SaaS platform that includes microservices, a frontend, multi-cloud infrastructure, enterprise authentication, billing, and organization management. However, what makes this role unique is that our platform delivers AI inference. Validating whether inference works well is a problem that traditional QA methods do not fully solve. A deployment can succeed technically and still produce poor inference.
We are looking for a dedicated QA engineer who can own the product's quality, ensuring our product works the way any well-run SaaS platform should, while also developing the approaches needed to validate AI inference quality, model deployments, and integrations that traditional testing alone cannot cover.
Key Responsibilities- Own quality across FriendliAI's full platform stack: backend microservices, frontend, model deployments, and inference pipelines.
- Build and maintain automated test suites using pytest, covering unit, integration, and regression testing across backend services.
- Develop and run load and scalability tests using Locust to validate platform performance under real-world conditions.
- Own frontend and end-to-end testing with Playwright across the full user-facing product.
- Design and implement test strategies that account for LLM inference.
- Work closely with infrastructure and backend engineers to validate model deployment workflows, multi-cloud orchestration, and service integrations.
- Identify coverage gaps, prioritize test investment, and build tooling and pipelines.
Qualifications- 3+ years of experience in software quality engineering, with a track record of owning test strategy.
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent.
- Proficiency in Python and hands-on experience with pytest for test automation.
- Experience with load and performance testing tools such as Locust.
- Experience with browser automation and end-to-end testing frameworks such as Playwright.
- Working knowledge of LLM serving.
- Strong experience testing distributed systems with multiple interconnected components.
- Strong systems thinking.
- Comfortable working in a fast-moving environment.
Preferred Experience- Familiarity with AI infrastructure or model serving systems
- Experience building QA infrastructure from scratch in an early-stage or scaling environment.
- Background in performance and scalability testing for cloud-native or multi-cloud systems.
- Experience covering both backend and frontend testing in a single role.
- Exposure to observability tooling and how it supports debugging and quality validation.
Benefits- Flexible working hours
- Daily lunch and dinner provided; unlimited snacks and beverages
- Supportive and highly collaborative work environment
- Health check-up support and top-tier equipment/hardware support
- A front-row seat to the generative AI infrastructure revolution
- Competitive compensation, startup equity, health insurance, and other benefits.
