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Job Summary We are looking for a Validation Engineer to validate and qualify AI software, firmware, hardware, and system-level features for edge AI accelerator platforms. The role involves designing validation strategies, developing automation frameworks, executing functional and performance testing, and ensuring high-quality releases across embedded and AI workloads. The ideal candidate should have robust experience in Linux systems, automation, AI/ML validation, CI/CD workflows, and debugging complex hardware-software interactions. Key Responsibilities Design and execute validation plans for AI accelerator platforms, embedded systems, and SDK releases. Validate AI/ML workloads including LLMs, VLMs, vision, audio, and NLP models across different hardware targets. Develop and maintain automation frameworks using Python, Bash, Jenkins, and CI/CD pipelines. Perform functional, regression, stress, performance, and scalability testing. Validate PCIe, USB, networking, memory, storage, and system-level interfaces. Debug issues across firmware, kernel drivers, runtime, and application layers. Work closely with software, compiler, runtime, hardware, and DevOps teams for issue triaging and release qualification. Build automated validation pipelines for nightly, release, and PR-based testing. Analyze logs, crashes, performance bottlenecks, and system stability issues. Create detailed validation reports, dashboards, and quality metrics. Support customer-specific validation and benchmarking activities. Manage and maintain lab infrastructure, devices, and automated execution environments. Validate deployments across x86, ARM, Linux, containerized, and cloud-based settings. Ensure release readiness through comprehensive test coverage and execution. Required Skills Experience: 5+ years in Validation, QA Automation, Embedded Systems, or AI Platform Testing. Programming: Python, Bash, C/C++; Linux, Embedded Linux, Shell Scripting. Automation & DevOps: Jenkins/GitHub Actions, Docker, Git, CI/CD. Validation: Functional, Regression, System, AI/ML Model Validation, Performance Benchmarking, Stress & Reliability Testing. AI/Embedded: OpenCV, TensorFlow, PyTorch, ONNX; Edge AI, LLM/VLM validation (preferred). Hardware Validation: PCIe, USB, Networking, Kernel Drivers, Hardware-Software Integration. Tools: Jira, Confluence, Redis. Preferred: Experience in AI accelerators/Semiconductor companies, compiler/runtime validation, inference frameworks, and distributed automation. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.