[QA Expert] Lead the Systemization and Strategy Planning of "AI Native QA" for "Tabelog" Used by Over 98 Million Monthly UsersTranslated from Japanese

▼Job Description In the QA operations for a specific domain within Tabelog's existing business, you will establish knowledge and standards within the framework of harness engineering to enable AI to accurately judge quality, and then review and approve the AI's judgment results. 【Mission】 The Quality Management Department is a team that promotes the long-term vision "Continuous Quality by AI (CQ by AI)", which aims for "QA operations to be consistently handled by AI". You will participate as an expert who transfers QA knowledge and standards to the AI so that it can correctly judge quality. This initiative goes beyond simply streamlining testing; it transforms QA, which was a "pre-release checkpoint," into an autonomous mechanism embedded within the development process itself. For Tabelog, used by tens of millions of users monthly, we aim to achieve quality assurance where AI agents handle code reviews, risk analysis, and E2E tests, with only final approval performed by humans. We have already achieved a code generation accuracy of 97%, a 52% reduction in test execution effort, and a 64% automation rate through automatic test coding, and we are now in the phase of building the next stage together. This position is responsible for establishing QA domain knowledge as guardrails, checklists, and standards within the framework of harness engineering to enable AI to make correct quality judgments. While traditional QA engineers "design and execute tests themselves and judge quality," this position shifts the role to "designing and verifying the criteria for AI judgment, and reviewing the AI's judgment results." You will leverage your expertise as a QA engineer to take on the role of "teaching quality to AI," a job that still has few precedents in the industry. 【Specific Job Duties】 ■ Establishing QA Knowledge and Standards - Establish knowledge so that AI can consistently execute QA operations for the assigned domain, from test analysis to test design, test execution, and test report creation. - Specifically, convert implicit knowledge regarding QA judgment criteria, such as "what constitutes a good test," "how to assess risk," and "judging updates to test perspectives based on changes," into explicit forms like contexts and guardrails that AI can reference. ■ Reviewing and Approving AI's Judgment Results - Conduct sampling reviews of AI's judgment results from QA operations (risk analysis, code review, E2E tests) performed by AI. - Take final responsibility for high-risk cases, exceptional cases, and suspected AI output vulnerabilities (prompt injection, hallucination, etc.) that cannot be solely entrusted to AI. ■ Involvement in the Feedback Loop - Review triage results of defects/incidents and ODC analysis results, and decide on the adoption of recurrence prevention measures. - Analyze the causes of cases where AI made incorrect judgments, reflect them in knowledge/rules, and create an environment where AI does not repeat the same mistakes. ■ QA Operations for Projects (conducted in parallel) - Take responsibility for test design, execution, and quality judgment for each project, making judgments that AI cannot handle for high-risk and complex projects. - Accumulate perspectives and judgment criteria gained through QA operations as a knowledge base for CQ by AI. Image of Post-Hire and Career Path ■ Immediately After Joining (0-3 months) - Understand the QA operations, service specifications, and business structure of the assigned domain. - Inventory current test perspectives and quality standards, identify missing knowledge for AI-driven QA, and proceed with establishing it. ■ 3-6 Months Later - Proactively lead knowledge establishment for the assigned domain and become capable of independently performing AI sampling reviews. - Reach a state where the AI's judgment accuracy improvement cycle can be autonomously managed. ■ 6 Months to 1 Year Later - Become independent as an AI-native QA engineer for the assigned domain. - Establish a system for maintaining quality by sharing judgment responsibilities with AI for high-risk and exceptional cases. ■ Career Path - AI-Native QA Expert (Senior Specialist Role): A senior specialist role leading knowledge design for multiple domains and standardization of AI judgment criteria. - AI and QA Collaboration Designer: Transition to a position where you design what to delegate to AI and what to retain by humans. - You will be in a position to set a precedent for a new career path of "teaching quality to AI" as a QA engineer. AI-specialized QA roles have higher market value compared to traditional QA, and choosing this career path directly leads to an increase in market value. ▼Required Skills/Experience - 3+ years of practical experience in the upstream QA process (test strategy formulation, test planning, risk analysis). - Experience leading quality standard setting, test design review, and quality judgment of test execution results as a Test Lead or QA Manager. - Experience designing and leading the improvement cycle of test quality through root cause analysis of defects and incidents. - Experience using Claude Code (or AI coding agents like Cursor) as a daily development environment for over 6 months. - Experience designing instructions (prompts, rules, standards) for AI and applying them to QA operations to improve processes. ▼Preferred Skills/Experience - Experience designing and implementing E2E tests using Selenium/Appium etc. - Experience in data analysis and log analysis using SQL/BigQuery. - Knowledge and experience regarding quality risks of AI output, such as prompt injection and hallucination. - Knowledge and experience regarding metrics and frameworks for quantitatively evaluating AI output. ▼Desired Candidate Profile - Someone who can think with ownership about what is needed for their assigned domain and take initiative. - Someone who can bring their own solutions when facing challenges. - Someone who can consciously move between abstract levels of purpose and requirements and concrete levels of implementation and procedures. - Someone who can detect uncertain risks early and proactively address them. - Someone who can separate emotions from logic in discussions and turn disagreements into constructive dialogue. - Someone who can build consensus on quality standards across teams and organizations and drive them forward by involving others. - As a QA expert, someone who can communicate quality perspectives to engineers and PdMs and elicit their judgments. ▼Job Appeal 【Being at the Forefront of QA Engineer Careers】 From "executing tests" to "designing the criteria for AI to judge quality." You can be involved as a designer at this turning point where the role of QA is changing. 【A Job No One Has Done Before: Formalizing Tacit Knowledge】 The job of converting "what constitutes a good test," gained from experience by skilled QA engineers, into a form that can be given to AI is not yet established in the industry. Tabelog has already achieved 97% code generation accuracy through the formalization of QA knowledge. The next step is to expand this knowledge base to the upstream processes of risk analysis and test design. We will create the precedent ourselves. 【An Unprecedented Initiative to Eliminate Manual QA Work in a Large-Scale Service】 Once CQ by AI is completed for Tabelog, used by tens of millions of users monthly, you will be centrally involved in this unprecedented initiative to eliminate manual QA work. 【Define Your Own New Vision of QA Collaboration with AI】 As a supervisor of AI judgments, you can think and design for yourself what to delegate to AI and what to retain by humans. ▼Development Environment ■ AI Agents: Claude Code (SKILL / Sub-agent / Hooks / Agent Teams) / Devin / MCP Server ■ Test Automation: Selenium, Appium, Cucumber, Playwright ■ CI/CD: CircleCI, Bitrise ■ Data Platform: BigQuery, Tableau ■ Task Management & Analysis: Asana ■ Communication: Slack, Teams, GitHub, Confluence, Miro ■ Development Tech Stack: Ruby on Rails, React.js, Next.js, Swift/SwiftUI, Kotlin ▼Employment Type Full-time employee (No fixed term) ▼Probationary Period 3 months *No change in employment status or treatment during the probationary period. ▼Salary Annual salary: ¥7,500,000 - ¥12,000,000 ▼Bonus Bonus once a year (June) ▼Annual Salary Lower Limit (10,000 JPY) 750 ▼Annual Salary Upper Limit (10,000 JPY) 1200 ▼Work Location Head Office: 3-5-7 Ebisu Minami, Shibuya-ku, Tokyo, Digital Gate Building *We adopt a hybrid work style that flexibly combines remote work and office attendance depending on the nature of the work and the team's situation. ▼Working Hours Flextime work (Standard working hours: 8 hours per day) - Core time: 10:00 - 15:00 *Overtime may occur due to work circumstances. *This differs for management supervisors and those under discretionary labor systems. *You can flexibly change your start and end times according to the team's situation and your own work pace. ▼Break Time 1 hour during working hours ▼Overtime Work Yes ▼Holidays, Paid Leave, Special Leave Complete two-day weekend system - Scheduled holidays: Saturdays, Sundays, national holidays, year-end and New Year holidays (12/29 - 1/3) - Leave: Summer vacation (3 days), paid leave, congratulatory/condolence leave *Paid leave: Up to 10 days granted depending on the month of joining. ▼Benefits Kakaku.com aims to be a company where employees can experience both "the joy of working," which we have valued since our founding, and "ease of working." We will continue to actively review our systems, reflecting employee feedback and considering usage status. - Social insurance complete (Employment, Health, Workers' Compensation, Pension) - Defined contribution pension plan - Group life insurance - Free comprehensive medical check-ups (regular health examinations) - EAP counseling program - Congratulatory/condolence money - Maternity leave - Childcare leave (can be taken until the end of the fiscal year in which the child turns 3 years old) - Reduced working hours for childcare (can be taken for up to 12 years until the child graduates from elementary school; coreless flextime system available) - Child nursing leave (10 days per year, 5 of which are paid leave; if multiple children, 20 days per year, 10 of which are paid leave) - Nursing leave - Volunteer leave - Family allowance (payment conditions apply) - Remote work environment allowance ▼Allowances - Commuting expenses fully reimbursed (with limits) - Family allowance (payment conditions apply) - Remote work environment allowance ▼Insurance Enrollment - Social insurance complete (Employment, Health, Workers' Compensation, Pension) - Group life insurance ▼Measures to Prevent Passive Smoking at the Workplace No smoking indoors in principle (smoking area available) ▼Education/Skill Improvement/Career Advancement - Self-study support system available [Support for Engineers] - Study sessions held Company-wide study sessions, including those for Kakaku.com, are held once every three months. Study sessions for all Tabelog engineers are held on average once a month. Irregular study sessions are also held within each engineer team at Tabelog. - Purchase of technical books Technical books can be freely purchased within the budget, although they become company assets. Long-term loans are also possible. Many engineers purchase technical books every month. - Seminar participation Participation is possible during working hours. If you wish to attend a paid seminar, the company will cover the costs after prior consultation. - Support for organizing internal and external events We provide support such as venue provision and sponsorship. Tabelog sponsors the annual RubyKaigi. ▼Health - Free comprehensive medical check-ups (regular health examinations) - EAP counseling program ▼Interview/Selection Process Document screening → 1st interview → Final interview → Offer *The selection flow is subject to change.