Staff embedded ml/dsp systems engineer (audio engineering)

Experteer Overview As 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 / Benefits Architect and optimize end-to-end deployment pipelines for compact audio AI models on DSP/NPU targets Define DSP/NPU partitioning strategies balancing workload, memory, latency, and power across the So C Own simulation-to-RTL validation flows with bit-exact reference models and RTL co-simulation Implement and optimize fixed-point signal processing and neural network kernels for efficient inference Profile and optimize inference performance under always-on, real-time constraints for hearables/wearables Design and maintain model compression/quantization workflows (PTQ, QAT) with quality tracking Develop array processing algorithms (beamforming, spatial filtering) from prototype to fixed-point deployment Contribute to audio ASIC system architecture decisions based on algorithmic and deployment needs Generate IP and represent technical depth to OEM customers in automotive and hearable segments Mentor engineers in deployment practices and hardware-aware algorithm design Responsabilità Masters/Ph D in Electrical Engineering, signal processing, or related field 6+ years in audio/speech signal processing within semiconductor environments Hands‑on deployment experience on DSP and/or NPU platforms Expertise in fixed‑point algorithm implementation and model quantization (PTQ/QAT) Strong knowledge of simulation-to-RTL flows and bit‑exact modeling Proficiency in C (embedded/firmware), Python, MATLAB, and deep learning frameworks (Tensor Flow/TFLite, Py Torch/ONNX) Experience with low‑level profiling tools, ISA, and memory optimization for embedded AI Requisiti fondamentali competitive compensation and benefits work-life balance opportunity to work on cutting‑edge projects #J-18808-Ljbffr