Experteer OverviewAs Staff AI/ML Embedded ML/DSP Systems Engineer, you will lead the architecture and optimization of real-time audio AI systems across industrial, data center, and wearables domains. You work at the hardware-software frontier, shaping DSP/NPU deployment, and guiding model compression and fixed-point implementations. You'll collaborate with RTL and ASIC teams to ensure hardware-aware algorithm design and robust validation. The role offers mentorship, strategic impact on AI/ML architectures, and involvement in cutting-edge audio processing. This is a chance to contribute to Analog Devices' mission at the Intelligent Edge and advance Physical AI initiatives.Retribuzione / BenefitsArchitect and optimize end-to-end deployment pipelines for compact audio AI models on DSP/NPU targetsDefine DSP/NPU partitioning strategies balancing workload, memory, latency, and power across the SoCOwn simulation-to-RTL validation flows with bit-exact reference models and RTL co-simulationImplement and optimize fixed-point signal processing and neural network kernels for efficient inferenceProfile and optimize inference performance under always-on, real-time constraints for hearables/wearablesDesign and maintain model compression/quantization workflows (PTQ, QAT) with quality trackingDevelop array processing algorithms (beamforming, spatial filtering) from prototype to fixed-point deploymentContribute to audio ASIC system architecture decisions based on algorithmic and deployment needsGenerate IP and represent technical depth to OEM customers in automotive and hearable segmentsMentor engineers in deployment practices and hardware-aware algorithm designResponsabilitàMasters/PhD in Electrical Engineering, signal processing, or related field6+ years in audio/speech signal processing within semiconductor environmentsHands‐on deployment experience on DSP and/or NPU platformsExpertise in fixed‐point algorithm implementation and model quantization (PTQ/QAT)Strong knowledge of simulation-to-RTL flows and bit‐exact modelingProficiency in C (embedded/firmware), Python, MATLAB, and deep learning frameworks (TensorFlow/TFLite, PyTorch/ONNX)Experience with low‐level profiling tools, ISA, and memory optimization for embedded AIRequisiti fondamentalicompetitive compensation and benefitswork-life balanceopportunity to work on cutting‐edge projects
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