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 SoC
* 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/PhD 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 (TensorFlow/TFLite, PyTorch/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
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