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 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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