Overview:
At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges.
Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.
You’ll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about—locally and globally.
Come make an impact every day at Zebra.
What We're Looking For:
We are aggressively expanding our Edge AI capabilities with the upcoming rollout of our new NVIDIA Jetson Orin Edge Box deployments and their companion mobile applications. To ensure the robustness, scalability, and real-time performance of this complex ecosystem under heavy user load, we are establishing a dedicated Quality Assurance function. We need an experienced QA Test Lead to spearhead this initiative, define the overarching test strategy, and guide a specialized testing team.
Overview
We are seeking a highly skilled and strategic QA Test Lead to direct the validation of our next-generation AI ecosystem. In this role, you will own the end-to-end quality assurance strategy for a complex system that spans hardware, edge computing, and mobile platforms. You will lead the QA lifecycle for our NVIDIA Jetson Orin Edge Box deployments, companion Android and iOS applications, and the backend infrastructure. A large part of the job requires directing and executing extensive benchmarking of models running on edge devices, particularly understanding system behavior under heavy concurrent usage. The ideal candidate possesses a strong blend of team leadership, advanced automation architecture skills, performance testing expertise, and the ability to drive a forward-looking approach to utilizing AI tools in the QA process.
Key Responsibilities
- Leadership & Test Strategy:
- Define, own, and execute the comprehensive test strategy for the Edge AI and Mobile ecosystem.
- Lead, mentor, and task-manage QA automation engineers, guiding them in best practices for script development and testing methodologies.
- Act as the primary QA point of contact, collaborating cross-functionally with product managers, ML researchers, and hardware engineers to establish quality gates and acceptance criteria.
- Provide clear reporting on quality metrics, test coverage, and release readiness to senior management and stakeholders.
- Edge Device Validation & Benchmarking (NVIDIA Jetson Orin):
- Lead the validation of deployment, stability, and inference accuracy of AI models running on NVIDIA Jetson Orin edge devices.
- Oversee and conduct extensive benchmarking of AI model performance. Evaluate system impact, thermal performance, and inference latency when a high number of concurrent users are accessing the edge device to run various models.
- Data Augmentation & Computer Vision Testing:
- Direct the use of OpenCV to augment video test data, creating synthetic conditions and edge cases not included in originally captured test datasets to ensure highly robust model validation.
- Establish workflows for utilizing automated video annotation tools (such as CVAT) to build, manage, and refine ground truth datasets for testing.
- Mobile App Testing (Android):
- Architect comprehensive manual and automated testing strategies for companion Android applications.
- Ensure rigorous validation of UI/UX, BLE/Wi-Fi connectivity with the edge box, and real-time data streaming capabilities.
- Automation Framework Architecture:
- Design the architecture for robust, scalable automated test frameworks for both mobile (Appium, Espresso) and embedded Linux/Edge platforms (Python, Pytest).
- Drive the integration of automated tests into the CI/CD pipeline (e.g., Jenkins, GitLab CI) to ensure continuous validation and fast feedback loops.
- Load & Performance Testing:
- Architect load, stress, and endurance test plans simulating high-throughput environments and concurrent edge-to-cloud connections.
- Identify architectural bottlenecks and oversee the measurement of system latency using tools like JMeter.
- AI-Driven Testing & Tools:
- Drive the adoption of modern AI testing tools to accelerate test script generation and maintenance across the team.
- Establish processes using platforms like Weights & Biases (W&B) to track model evaluation metrics and validate ML outputs across software builds.
Qualifications
Required:
- Experience: 7+ years of experience in Software Quality Assurance, Systems/Solutions Testing, or SDET roles, with at least 2+ years in a Lead, Principal, or QA Management position.
- Leadership: Proven track record of defining test strategies, leading agile QA efforts, and successfully delivering complex, multi-platform projects.
- Operating Systems: Expert-level knowledge of Linux, specifically in the context of embedded systems, edge deployments, and hardware-software integration.
- Edge/IoT: Extensive experience testing embedded Linux systems, IoT devices, or edge computing hardware. Direct experience with NVIDIA Jetson platforms (Nano, Xavier, or Orin) is essential.
- Computer Vision & ML Tools: Hands-on expertise with OpenCV for video augmentation. Deep familiarity with automated video annotation tools (like CVAT) and model performance tracking platforms (like Weights & Biases).
- Performance/Load Testing: Advanced understanding of load testing methodologies, specifically regarding the concurrency limits of edge hardware under high user access. Hands-on experience with performance tools (like JMeter).
- Mobile Automation: Strong architectural knowledge of mobile test automation for both Android and iOS using Appium or native frameworks.
- Programming: Advanced coding skills in Python (essential for edge/AI/OpenCV tasks) and familiarity with Java/Kotlin.
- Networking/Protocols: Solid understanding of networking protocols relevant to IoT and streaming (TCP/IP, MQTT, REST, WebRTC, RTSP).
Nice-to-Have:
- Experience with NVIDIA SDKs (TensorRT, DeepStream, CUDA).
- Advanced experience testing high-throughput video streaming applications.
- Familiarity with containerization on edge devices (Docker, Kubernetes).
Benefits:
We understand the importance of work-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well-being day to promote revitalization and success.
Job Posting Statement:
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AI Technology Statement:
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