Senior Embedded Software Engineer

What you'll do

• Evaluate CPUs, GPUs, NPUs, and embedded computing platforms to identify the optimal architecture for future measurement devices
• Benchmark computer vision and deep learning workloads across multiple hardware platforms to drive hardware selection and cost optimization
• Prototype and validate edge AI solutions using platforms such as NVIDIA Jetson, Intel Atom, Coral TPU, Rockchip NPU, Qualcomm, and emerging accelerator technologies
• Deploy, optimize, and support multiple computer vision and machine learning models running concurrently on embedded devices
• Analyze system performance including compute utilization, memory consumption, thermal characteristics, power efficiency, and inference latency
• Develop software and systems-level applications in Python and C/C++ to support measurement, device management, and computer vision models
• Work closely with Linux operating system components, including system services, kernel interfaces, device drivers, and hardware integration layers
• Collaborate with Computer Vision, Data Science, and Platform Engineering teams to translate research models into scalable edge deployments
• Rapidly prototype, test, and iterate on hardware and software solutions to improve device capabilities while reducing platform costs
• Influence the long-term hardware roadmap through hands-on evaluation, proof-of-concept development, and technical recommendations

Requirements

• Bachelor’s degree in Computer Science, Mathematics, Electrical Engineering, or a related technical field
• 5+ years of experience developing embedded, IoT, edge computing, or embedded AI systems
• Experience deploying computer vision or deep learning models on edge devices
• Strong understanding of microprocessors (CPU, GPU, etc.), memory, storage, and system architecture tradeoffs
• Strong Python development skills and ability to rapidly prototype and validate technical concepts
• Experience developing embedded applications and systems software in C/C++
• Familiarity with Linux kernel interfaces, system services, device drivers, and low-level system troubleshooting
• Experience developing on TensorFlow or PyTorch
• Experience with AI inference optimization technologies such as TensorRT, OpenVINO, ONNX Runtime, TensorFlow Lite, or similar frameworks

Tech stack

Benefits