(Staff/ Sr. Staff) Machine Learning Engineer

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Description

Description: Lead applied research and strategic definition of machine learning algorithms, quantization methodologies, and toolchain capabilities for the Neural Network Development Kit (NDK) roadmap targeting next-generation Edge AI compute solutions Drive innovation at the intersection of ML algorithms and constrained hardware environments, identifying and validating the latest Edge AI technologies applicable to product requirements Serve as the primary technical interface between the AI Architecture team and the Software (Edge AI) team, delivering well-researched toolchain feature proposals and algorithmic specifications for implementation Collaborate with Software (Edge AI) and AI Architecture teams to identify and pursue targeted improvements in ML software methodology, and support Software-initiated improvement efforts with algorithmic insight and implementation guidance Maintain deep engagement with the global Edge AI research community to ensure the NDK roadmap reflects the state of the art in model efficiency, compression, and on-device learning Key Responsibilities Collaborate with the Sr. , MLIR, TVM, ONNX Runtime) as acceleration vectors for NDK development ), and the trade-offs between model accuracy, computational complexity, and memory footprint Demonstrated ability to stay at the forefront of the Edge AI research community, with a track record of translating academic and industry advances into practical product roadmap contributions Hands-on experience with mainstream ML frameworks (PyTorch, TensorFlow/Lite) and familiarity with ML compiler stacks such as MLIR, TVM, or ONNX Runtime Experience consuming hardware architectural specifications and translating them into software toolchain requirements and algorithmic optimizations Excellent communication skills with ability to present complex research findings and toolchain proposals clearly to architecture, software, and executive audiences Strong analytical and problem-solving abilities with emphasis on quantitative benchmarking, accuracy-efficiency trade-off analysis, and performance profiling on target hardware Demonstratedability to work collaboratively across team boundaries, including assembling and coordinating cross-functional task forces without direct authority Familiarity with RISC-V ISA and its software ecosystem, particularly in the context of AI inference deployment Experience with FPGA-based or simulator-based prototyping to validate algorithmic concepts against pre-silicon hardware models (preferred but not required) Self-motivated with ability to work independently, lead applied research initiatives, and drive toolchain innovation from algorithmic exploration through specification and successful team handoff

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