Engineer II, QA

About the role

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.

What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.

Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small.

Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!

About the Role

We're pioneering a new role focused exclusively on quality assurance for GenAI systems. As our AI QA Engineer, you'll develop testing strategies for full stack application testing, evaluation frameworks, and quality metrics specifically designed for LLM-powered applications. This role requires a unique blend of QA expertise, understanding of GenAI behaviour, and automation skills

to ensure our AI features are reliable, accurate, and trustworthy

Key Responsibilities

- Design and implement comprehensive testing strategies for GenAI features including conversational AI, agentic systems, and LLM-powered workflows

- Develop automated test suites for prompt testing, including regression tests that detect unintended changes in model behaviour

- Create evaluation frameworks to measure GenAI quality across multiple dimensions (accuracy, relevance, safety, consistency, latency)

- Build and maintain test datasets and golden examples that represent diverse user scenarios and edge cases

- Implement monitoring and alerting systems to detect quality degradation in production GenAI features

- Perform adversarial testing to identify potential failures, hallucinations, biases, or security vulnerabilities in AI systems

- Collaborate with engineers to define acceptance criteria and quality gates for AI feature releases

- Develop tools and frameworks that make it easy for engineers to test their GenAI implementations

- Conduct user acceptance testing and gather feedback on AI feature performance from internal users

- Document testing procedures, known issues, and quality metrics in clear, accessible formats

- Partner with Product and Design teams to ensure AI features meet user experience standards

- Stay current with GenAI testing methodologies, tools, and industry best practices