Job description

We are looking for a Quality Commander to build and lead our QA function. At KoiReader, you won't just be finding bugs; you will be defining the quality standards for complex AI systems that run on Mobile, Desktop, and Edge devices. As the QA Lead, you will architect automation frameworks, mentor QA Engineers, and act as the ultimate gatekeeper for our Computer Vision models and applications. If you are passionate about building a culture of quality from the ground up in a high-growth AI startup, this is your role.

Key Responsibilities

- QA Strategy & Leadership: Define and implement the overall QA roadmap. Mentor a growing team of QA Engineers, establish best practices, and drive a culture of continuous quality improvement.
- Visual & AI Quality Assurance: Pioneer testing methodologies for our Computer Vision AI. Define how we test and validate Image/Video inputs, lighting conditions, and model accuracy at scale.
- Cross-Functional Alignment: Partner with Product Managers and Developers early in the SDLC to identify edge cases and ensure testability is built into the product design.
- Release Gatekeeping: Own the "Go/No-Go" decision for production releases.
- Metrics & Reporting: Define QA KPIs (Defect Escape Rate, Automation Coverage). Build dashboards to give stakeholders clear visibility into product health and release readiness.
- Domain Knowledge: Familiarity with Logistics or Supply Chain operational challenges.

Skills and Requirements

- Experience: 5+ years in Software Quality Assurance, with at least 2 years in a Lead or mentoring capacity.
- Strong Communication Skills: Ability to clearly articulate QA findings, influence “Go/No‑Go” decisions, and collaborate across Product, PM, and DevOps teams. Essential for mentoring QA Engineers and ensuring quality is embedded early in the SDLC.
- Mobile & Cross-Platform: Proven track record of leading testing for mobile apps and complex cross-OS desktop environments (Windows, Linux).
- AI/ML Testing: Previous experience testing AI, Machine Learning, or Computer Vision models.
- Computer Vision Model (CV) Testing: Hands-on experience testing AI models for Object Detection, Image Classification, and Semantic/Instance Segmentation, specifically using live and pre-recorded video feeds to validate model accuracy, edge cases, and performance under varying lighting and environmental conditions.
- Analytics & Dashboard QA: Extensive experience testing complex, data-heavy web dashboards, ensuring end-to-end data accuracy, real-time widget updates, chart rendering, and filter logic.
- Data Analysis & Reporting: Strong proficiency in Microsoft Excel (Pivot Tables, VLOOKUPs, macros) to analyze test results, manage complex test data sets, and generate actionable QA metrics
- Test Management & Bug Tracking: Proficient in documenting test plans, tracking defect lifecycles, and managing QA workflows using tools like Jira.
- Documentation & Visualization: Proficient in using Microsoft Office Suite (Word, PowerPoint, Excel) and basic visualization tools (Paint, Snagit) to draft clear test plans, document step-by-step bug reproduction guides, and present QA metrics to technical and non-technical stakeholders.

Brownie Points

- Automation Mastery: Deep hands-on coding experience (Python, Java, or JavaScript) and expertise in automation tools (Selenium, Appium, Playwright, or Cypress).
- API & Backend: Strong experience validating complex backend architectures and APIs using tools like Postman.
- Automation Architecture: Transition our testing efforts by building scalable, robust automation frameworks from scratch for Mobile (iOS/Android), Web Apps.

What We Offer

- Ownership & Leadership: Lead a performance-oriented team. You have the autonomy to choose your tech stack and define the QA culture.
- Meritocracy: A candid startup culture where the best ideas win and performance is heavily rewarded.
- Impact: Design QA frameworks for high-accuracy systems that physically move the global supply chain.

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