Friendli 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 ResponsibilitiesOwn 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.
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.
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.
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.
FriendliAI is building the world’s best AI inference platform that makes large language and multi-modal models fast, efficient, and deployable at scale. We power high-throughput, low-latency AI workloads for organizations worldwide and integrate directly with Hugging Face, giving developers instant access to over 500,000 open-source models.
We are a small, fast-moving team doing work that matters at one of the most exciting moments in the history of technology. With our world-class inference engine, we are building a platform that the AI industry can actually rely on.
Skills Required
- 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.
- 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.
What We Do
FriendliAI is The Frontier AI Inference Cloud: an AI infrastructure platform that deploys, scales, and monitors large language and multimodal models. Its inference engine maximizes GPU utilization to deliver faster performance and steep cost savings for open-weight and custom models, while offering enterprise-grade reliability, SLAs, and compliance to help teams run generative AI and agent workloads at production scale.
