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Tier-1 brand and metro location increase interest, but PhD-level niche on-device ML reduces applicant density.
On-device ML, SoC/ARM, compiler and low-level optimization expertise is highly domain-specific and not easily transferable.
PhD requirement, patents, and deep ML plus low-level systems expertise create highly selective hiring filters.
Design and implement a neural acceleration platform for on-device ML inference and training, optimizing for system KPIs and quality.
Lead development of machine learning algorithms tailored for mobile device use cases, ensuring high code quality through predictable processes.
Collaborate with product owners and managers to refine backlog and identify platform enhancement opportunities leveraging existing capabilities.
Ph.D in machine learning or related field; or advanced degree with equivalent industry experience.
Experience in model optimization techniques including compression, quantization, and neural architecture search, with good knowledge of LLM and LVM for on-device.
Proficient in Python, C++, Linux, and deep learning frameworks such as Pytorch or Tensorflow.
Strong understanding of processor architectures (ARM v7 & v8 big.LITTLE), multi-threading, multicore programming, and machine learning algorithms.
Demonstrated expertise in advancing on-device ML platforms with measurable system performance improvement.
Proven track record in research excellence indicated by publications and patents in machine learning or neural acceleration.
Experience working with complex system-level ML integration involving SoC capabilities and cross-disciplinary knowledge of hardware and software optimizations.