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For Companies At a Glance
- Tasks: Validate and enhance AI features for our next-gen video dashcam platform.
- Company: Join Xirgo, a leader in smart fleet logistics and IoT solutions.
- Benefits: Enjoy competitive salary, training, and growth opportunities.
- Other info: Collaborative environment with opportunities to work on innovative projects.
- Why this job: Make a real impact on fleet safety and efficiency with cutting-edge technology.
- Qualifications: 5 years in QA or systems engineering; strong skills in AI/ML and test automation.
The predicted salary is between 60000 - 75000 ÂŁ per year.
Driving The Future of Smart Fleet Logistics
At Xirgo, we’re not just transforming logistics — we’re redefining what’s possible. Let’s move forward together.
Our Purpose
We believe smarter tools create smarter operations. As the switched-on experts in IoT fleet solutions, we transform uncertainty into confidence, complexity into clarity, and data into decisions.
Our Vision
We empower partners with intelligent fleet logistics to create a more connected future. From bustling cities to open highways, from railroads to runways, our innovative technologies make peace of mind the new normal.
Our Mission
To be the world’s most trusted partner in smart fleet logistics, delivering comprehensive IoT solutions that transform data into useful information. We enhance fleet safety, efficiency, and performance—ensuring confidence at every step.
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.
QA (AI) Engineer employer: Xirgo Holdings, Inc
Contact Detail:
Xirgo Holdings, Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land QA (AI) Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to QA and AI. We recommend doing mock interviews with friends or using online platforms to get comfortable talking about your skills and experiences.
✨Tip Number 3
Showcase your skills through a portfolio or GitHub repository. We love seeing real examples of your work, especially if you’ve got projects related to AI/ML or embedded systems. It’s a great way to stand out from the crowd!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate individuals who want to drive the future of smart fleet logistics. Your next big opportunity could be just a click away!
We think you need these skills to ace QA (AI) Engineer
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI and quality assurance shine through. We want to see how your experience aligns with our mission of delivering reliable AI features that make a real difference in fleet logistics.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for the QA Engineer role. Highlight your relevant experience in embedded systems, AI/ML applications, and any specific tools you've used. This helps us see how you fit into our vision of smarter operations.
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate straightforward communication, especially when it comes to your skills and experiences.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at Xirgo!
How to prepare for a job interview at Xirgo Holdings, Inc
✨Know Your AI Inside Out
Make sure you brush up on your knowledge of AI and ML, especially in relation to computer vision and dashcam technologies. Be ready to discuss how you've validated AI features in previous roles and share specific examples of your experience with embedded systems.
✨Showcase Your Automation Skills
Since the role emphasises automation, prepare to talk about your experience designing and implementing test plans and suites. Bring examples of Python-based test frameworks you've developed or worked with, and be ready to explain how they improved testing efficiency.
✨Be Ready for Technical Challenges
Expect to face some technical questions or challenges during the interview. Brush up on your debugging skills and be prepared to discuss tools like Wireshark and JIRA. They might ask you to walk through a problem-solving scenario, so think of a few examples where you identified and resolved issues.
✨Collaboration is Key
This role involves working cross-functionally, so highlight your teamwork experiences. Prepare to discuss how you've collaborated with product managers and developers in the past, and think of ways you've driven process improvements in QA practices.