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Strong Tier-1 brand increases competition, but specialized embedded NPU ML skills narrow the candidate pool.
Embedded ML/NPU systems focus makes skills less transferable across industries.
Explicit minimum experience plus required embedded ML, C/C++, and NPU systems skills make filters stringent.
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Design, implement, and integrate ML software framework and kernels for Qualcomm's Low Power ML accelerator (NPU).
Develop and test ML operator kernels and end-to-end ML software performance on embedded Qualcomm Snapdragon platforms.
Collaborate with R&D and Systems teams for ML model optimization, system integration, use case testing, and commercialization support.
Bachelor's degree in Engineering, Computer Science, or related field with 2+ years Systems Engineering or related experience; OR Master's degree with 1+ year experience; OR PhD in related field.
Strong programming skills in C/C++ and Python required.
Experience developing and debugging embedded software, knowledge of ML operators (Transformers, LSTM, GRUs), multi-threaded programming (POSIX/PTHREADS).
Understanding of computer architecture, operating systems, data structures, algorithms, fixed point coding, and familiarity with ML frameworks like PyTorch, TensorFlow, or ONNX.
Experienced in developing embedded ML software for hardware accelerators, especially low power NPUs.
Proficient in ML inference optimization and model quantization/compression techniques.
Capable of working in dynamic, cross-disciplinary teams with strong problem-solving skills and technical communication abilities.