Analog Devices
Semiconductor
PrincipalAudio/DSPSystemsEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Principal Audio/DSP Systems Engineer at Analog Devices. Skills: Audio DSP, AI Model Deployment, Embedded Systems. Architect end-to-end deployment pipelines. Define DSP/NPU partitioning strategies”
What You'll Achieve.
maximizing utilization of MAC arrays; maximizing utilization of SIMD paths; maximizing utilization of on-chip memory hierarchies; maximizing inference performance; rigorous quality tracking against floating-point baselines; clear path from MATLAB/Python prototypes to deployable fixed-point implementations; informing hardware spec decisions
Industry & Context.
balancing workload allocation; memory bandwidth; latency; power; close gaps between algorithmic intent and hardware behavior; maximizing utilization; under strict always-on and real-time constraints
10% travel, export licensing review process may apply
What They're Looking For.
Must Have
PhD in Electrical Engineering, signal processing, 10+ years in audio/speech signal processing, semiconductor environment, hands-on deployment experience on DSP and/or NPU platforms, fixed-point algorithm implementation, model quantization (PTQ/QAT), cycle-level optimization for resource-constrained processors, simulation-to-RTL flows, bit-exact modeling, RTL co-simulation, functional verification collaboration, C (embedded/firmware level), Python, MATLAB, deep learning frameworks (TensorFlow/TFLite, PyTorch/ONNX), low-level profiling tools, instruction set architectures, memory optimization for embedded AI inference, array signal processing, beamforming, acoustic system design
Nice to Have
NPU/accelerator architectures, dataflow engines, weight-stationary/output-stationary designs, programming models, ASIC development cycles, algorithm freeze, tapeout, silicon validation, always-on, sub-mW audio processing, hearable, TWS, or wearable products, US patents in audio signal processing, embedded ML, technically leading a small engineering team
What You'll Do.
Architect end-to-end deployment pipelines
Define DSP/NPU partitioning strategies
Own simulation-to-RTL validation flows
Perform low-level implementation
Profile and optimize inference performance
Design model compression and quantization workflows
Develop signal processing algorithms
Contribute to audio ASIC system architecture
How You'll Work.
Team & Collaboration
collaborate with RTL teams; represent team's technical depth to OEM customers
Full Job Description
**About Analog Devices** Analog Devices, Inc. (NASDAQ: _[ADI](http://finance.yahoo.com/q?s=ADI)_ ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at _[www.analog.com](https://www.analog.com/)_ and on _[LinkedIn](https://www.linkedin.com/company/analog-devices/posts/?feedView=all)_ and _[Twitter (X)](https://x.com/adi_news)_. # **Principal Audio/DSP Systems Engineer** **Responsibilities:** * Architect and optimize end-to-end deployment pipelines for compact audio AI models from trained model through quantization, profiling, and production deployment on DSP/NPU targets * Define and drive DSP/NPU partitioning strategies, balancing workload allocation, memory bandwidth, latency, and power across processing elements on the SoC * Own simulation-to-RTL validation flows: develop bit-exact reference models, collaborate with RTL teams on functional verification, and close gaps between algorithmic intent and hardware behavior * Perform low-level implementation and optimization of signal processing and neural network kernels for fixed-point DSP and NPU instruction sets, maximizing utilization of MAC arrays, SIMD paths, and on-chip memory hierarchies * Profile and optimize inference performance (cycles, memory footprint, power) under strict always-on and real-time constraints typical of hearable/wearable devices * Design and maintain model compression and quantization workflows (PTQ, QAT) with rigorous quality tracking against floating-point baselines * Develop signal processing algorithms for array processing, beamforming
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