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Xirgo • Newtownabbey, Northern Ireland, United Kingdom
Role & seniority: QA Engineer (AI); mid-to-senior level (5+ years QA/systems eng; 3+ years AI/ML or computer vision)
Stack/tools: embedded/edge testing, Python-based test frameworks, HIL/simulation, firmware/cloud tests; telemetry/logging tools; Wireshark, Drewlinq, serial loggers, oscilloscopes, power analyzers; JIRA; TestRail; Postman
AI Quality & Integration: validate end-to-end AI features (computer vision, ADAS, DMS) across device/edge/cloud; build data-driven feedback to diagnose performance and tune models
AI Models Test Planning & Automation: design/implement test plans and reusable automation for embedded/edge AI and video analytics; own QA artifacts and coverage
Execution & Systems Validation: run functional, regression, stress, and performance tests on firmware features; instrument systems and analyze defects across firmware and cloud
5+ years in QA or systems engineering with embedded/IoT and/or 3+ years in AI/ML applications
Strong QA methodologies and testing types (functional, performance, stress, regression)
Experience with test automation (prefer Python) and embedded/edge frameworks
Ability to interpret hardware/software interactions; strong debugging, analytical, communication, and organizational skills
Able to work independently in a fast-paced, collaborative environment
Real-time video processing, edge AI, or dashcam experience
Vehicle/video telematic
About the role We’re hiring a QA Engineer (AI) to ensure our next‑generation video dashcam platform delivers reliable, high‑performance AI features that bring real‑world value to fleet operators and drivers. You’ll sit at the intersection of product, engineering, and customer success—validating AI/ML capabilities end‑to‑end and building automation that scales quality across embedded systems and cloud services. What you'll do AI Quality & Integration Validate end‑to‑end behavior of AI features (e.g., computer vision, ADAS, DMS) across device, edge and cloud. Build data‑driven feedback loops to diagnose performance issues, tune models, and improve real‑world outcomes. AI Models Test Planning & Automation
Think automation first: design and implement test plans, suites, and reusable automation for embedded/edge AI and video analytics. Create and maintain test documentation; own QA artifacts (plans, cases, reports) and ensure coverage across devices, platforms, and browsers. Develop and extend Python‑based test frameworks (or equivalent) for HIL (hardware‑in‑the‑loop), simulation, and in‑vehicle testing Execution & Systems Validation
Run functional, regression, stress, and performance tests on firmware features: ADAS, DMS, A/V recording, live streaming, OTA updates, GNSS/Wi‑Fi location, power management, and protocol stacks. Instrument systems to capture telemetry, logs, and metrics; analyze defects across firmware and cloud layers. Tooling, Debugging & Issue Management Use tools such as Wireshark, Drewlinq, serial loggers, oscilloscopes, and power analyzers to verify data flow and system stability. Document issues clearly in JIRA (or similar), with reproducible steps and evidence; collaborate with developers to resolve root causes. Collaboration & Customer Enablement Work cross‑functionally with product managers, software/firmware, and hardware teams to translate requirements into actionable testable deliverables. Proactively identify process gaps and drive continuous improvement in our verification and release practices. Qualifications / Experience Required Bachelor's or Master’s in Computer Science, Electrical/Electronic & Systems Engineering, or related field (or equivalent experience). 5 years in QA or systems engineering with embedded/IoT devices and/or 3+ years in AI/ML applications/computer vision. Strong grasp of QA methodologies, testing types (functional, performance, stress, regression), and best practices. Experience with test automation (preferably Python) and frameworks for embedded/edge systems. Ability to interpret hardware/software interactions; excellent debugging, analytical, communication, and organization skills. Comfortable operating independently in a fast‑paced, collaborative environment. Preferred Exposure to real‑time video processing, edge AI, or dashcam technologies. Experience with vehicle/video telematics, power optimization, or environmental robustness.
Tools proficiency: TestRail, Postman, Wireshark, log/telemetry analysis. Understanding of fleet safety, telematics, or automotive camera systems. Why Xirgo Build safety‑critical AI features that matter to drivers and fleets. Work across device, edge, and cloud, with real customers and real data. Competitive salary and benefits, plus training, development, and growth opportunities.