- This role sits at the intersection of embedded systems engineering and applied AI,and is ideal for someone who enjoys squeezing maximum performance out of heterogeneous compute architectures. Key Responsibilities
Position Overview
We are looking for a Senior Embedded Application Engineer - FPGA to design, develop, and optimize image processing applications deployed on FPGA GPU compute platforms. You will work closely with hardware, firmware, and data science teams to bring machine learning models from research to real-time, resource-constrained embedded environments. This role sits at the intersection of embedded systems engineering and applied AI, and is ideal for someone who enjoys squeezing maximum performance out of heterogeneous compute architectures.
Key Responsibilities
- Design, develop, and deploy AI/ML inference pipelines on embedded platforms combining FPGA and GPU compute elements.
- Optimize and port trained ML models (CNNs, RNNs, transformers, etc.) for real-time inference on edge/embedded hardware.
- Develop and maintain HDL/HLS-based accelerators (Verilog/VHDL or HLS in C/C++) for ML workloads on FPGA.
- Implement and tune GPU kernels (CUDA/OpenCL) for performance-critical ML operations.
- Collaborate with ML Engineers to convert and quantize models (TensorRT, TVM, ONNX, Vitis AI, etc.) for embedded deployment.
- Profile and benchmark system performance (latency, throughput, power) across FPGA and GPU subsystems, and drive optimization.
- Work with hardware teams on board bring-up, driver integration, and hardware-software co-design.
- Build and maintain embedded Linux/Yocto Petalinux-based application software interfacing with FPGA/GPU accelerators.
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