We are strengthening our quality engineering capabilities to support a rapidly growing, AI-driven product platform.
As our services scale in complexity and user impact, we need a QA Engineer who can own end-to-end quality, from strategy definition to production release. You will help us build modern, automated quality practices that keep pace with fast delivery cycles while maintaining high reliability.
This role is key to establishing robust QA foundations that can scale across multiple products and teams.
Technology Stack
- Web Server-side：Kotlin (Spring)
- Web Frontend：React
- Platform：AWS, Kubernetes
Responsibilities
Quality Ownership & Strategy
- Design and implement AI testing methodologies, including constructing AI agents and delivery pipelines that seamlessly integrate quality checks into the development lifecycle.
- Own product quality from initial development through to production across a growing product platform, ensuring each release meets defined quality standards.
- Define, refine, and champion quality strategies, test plans, and quality gates for releases and milestones, driving continuous improvement of QA processes.
- Review and guide quality coverage across all layers (unit, backend, database, API, UI), ensuring adherence to the Test Pyramid and industry best practices.
- Lead defect triage, root cause analysis, and risk assessment, proposing mitigation strategies before deployment to minimize production incidents.
- Establish standard processes for all test activities, bug tracking, and release decisions, ensuring alignment with long-term department QA goals.
Test Planning & Execution Strategy
- Architect and execute comprehensive testing strategies across pre-release, post-deployment, and live production stages to ensure functional and non-functional quality.
- Advance automation maturity using modern frameworks, while managing resilience and failover testing to guarantee system-wide integrity and stability.
- Create and maintain key test artifacts such as test plans, test cases, and traceability matrices, and oversee regression and integration test cycles.
- Provide transparent QA reporting that allows stakeholders to understand release readiness, risk levels, and test coverage.
- Partner closely with product and engineering teams to identify risks early and define clear, measurable acceptance criteria for all features.
Collaboration & Leadership (Shift-Left Focus)
- Collaborate with engineering, product, and DevOps teams to maintain a unified quality vision, embedding QA practices early in the development lifecycle.
- Lead QA engineers and mentor developers on effective test design, automation strategies, and risk-based testing approaches.
- Act as a quality advocate across the organization, promoting proactive testing, continuous improvement, and shared ownership of quality.
- Lead quality discussions during key project planning, design reviews, and release meetings to ensure quality considerations are addressed upfront.
- Lead quality discussions during key project planning, design reviews, and release meetings to ensure quality considerations are addressed upfront.
Requirements
- Over 5 years of professional software testing experience, with a strong focus on designing and scaling complex automation architectures (E2E, API).
- Deep hands-on experience in manual and non-functional testing, including scenarios that validate stability, resilience, and user experience.
- High proficiency with tools such as Playwright, Postman, REST Assured, and GraphQL for automation and API validation.
- Practical experience using AI-driven tools (e.g., Cursor, Claude, Playwright MCP) to streamline test development, execution, and analysis.
- Strong leadership orientation with experience in international collaboration and oversight across multiple products or teams.
- Expert-level skills in scripting languages and complex API validation, including full integration testing across all layers to maximize coverage.
- Proven capability to lead product quality through transparent reporting, automation roadmaps, and comprehensive release quality assessments.
- Extensive experience with Agile methodologies (Scrum/Kanban), CI/CD workflows, Git, and test management platforms such as TestRail or Zephyr.
- Excellent communication, analytical, and documentation skills, enabling you to clearly articulate risks, findings, and recommendations.
Nice to haves
While not specifically required, tell us if you have any of the following.
- Understanding of AI model evaluation metrics such as accuracy, precision, recall, and response quality, and how to apply them to testing.
- Exposure to performance or security testing using tools such as JMeter or K6.
- Familiarity with cloud-based environments, preferably AWS, and testing in containerized or Kubernetes-based architectures.
- ISTQB or equivalent QA certifications, especially those focused on AI or advanced testing methodologies.
Experience in AI development and/or experience in using AI tools to improve development processes. - Experience in AI development and/or experience in using AI tools to improve development processes.
- Money Forward recently announced our AI Strategy roadmap which focuses on improving AI-driven operational efficiencies, as well as integrating AI agents into our products to deliver better value to our users.
Compensation
¥6,900,000 ~ ¥11,004,000 annually.
