Job Title: Software Engineer, Machine Learning Software Stack
Job Type: Full Time
Job Location: Santa Clara, California
- We develop FPGA-based hardware, software, and systems solutions to accelerate critical applications in areas such as 5G wireless infrastructure, network switching, and datacenter services.
- We work in small highly motivated teams of domain experts across the full product range, from high-level systems software to digital and analog circuit design, to create innovative products that are first-to-market and solve critical business needs.
- You will drive the effort to develop a complete software stack for an FPGA-based machine learning inference accelerator card reference platform.
- You will adapt existing open-source and university software when possible, and develop new software from scratch as needed, to assemble a complete full-stack, end-to-end software solution.
- You will work closely with sales, marketing, systems engineering, EDA tool developers, and FPGA architects to support diverse use models from FPGA micro-architecture exploration, memory subsystem design optimization, place-and-route software verification, system prototyping, pre-sales demonstration development, and customer deployment and scaling.
- Prior experience is required working with a machine learning accelerator micro-architecture and ISA, as well as current knowledge of state-of-the-art research.
- You must have a background in open-source compiler hacking. Experience desired with compiler intermediate representations (IRs) and back-ends, JIT compilers, as well as kernel-mode and user-mode runtime environments and device drivers.
- Familiarity is desired with industry-standard machine learning frameworks, acceleration libraries, domain-specific languages, and with common DNN models.
- Two years of work or educational experience in machine leaning accelerator micro-architectures and compilers
- Skilled practitioner in C or Java.
- Experience in Python, Verilog, and System-C.
- Experience required in one of more of the following:
- Machine learning accelerators such as OpenTPU, NVDLA, VTA, EIC
- Compilers such as Glow, TVM, CLANG, LLVM, or GCC
- Machine learning frameworks such as TensorFlow, PyTorch, Caffe2, and Kera’s
- Acceleration libraries such as MX Net
- Domain-specific languages such as Halide and Spatial
- Common DNN models such as Alex Net, ResNet50, Inception, YOLO, RNN, and LSTM
- Embedded system runtime environments and device drivers.
- MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or Physics.
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