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# Staff Embedded ML/DSP Systems Engineer (Audio Engineering)

- **Company:** Analog Devices
- **Location:** Valencia, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Artificial Neural Networks, Audio Signal Processing, Beamforming, Program Optimization, Profiling, Data Centers, Firmware, Data Flow Control, Hardware Design, Python (Programming Language), MATLAB, Tensorflow, Signal Processing, Software Deployment, Systems Architecture, Alwayson, Application Specific Integrated Circuits, Pytorch, Delivery Pipeline, Deep Learning, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations
- **Published:** August 5, 2026
- **Apply:** https://www.jobleads.com/es/job/ec29b3df0044f6873b3291d30ff95ea34

## About the Role

* Masters/PhD in Electrical Engineering, signal processing, or related field
 * 6+ years in audio/speech signal processing within a semiconductor environment, with significant hands-on deployment experience on DSP and/or NPU platforms
 * Demonstrated expertise in fixed-point algorithm implementation, model quantization (PTQ/QAT), and cycle-level optimization for resource-constrained processors
 * Strong working knowledge of simulation-to-RTL flows: bit-exact modeling, RTL co-simulation, and functional verification collaboration with design teams
 * Proficiency in C (embedded/firmware level), Python, MATLAB, and deep learning frameworks (TensorFlow/TFLite, PyTorch/ONNX)
 * Experience with low-level profiling tools, instruction set architectures, and memory optimization for embedded AI inference, * Direct experience with NPU/accelerator architectures (dataflow engines, weight-stationary/output-stationary designs) and their programming models
 * Familiarity with ASIC development cycles - from algorithm freeze through tapeout and silicon validation
 * Background in always-on, sub-mW audio processing for hearable, TWS, or wearable products
 * Track record of patents in audio signal processing or embedded ML
 * Experience technically leading a small engineering team
 * Solid foundation in array signal processing, beamforming, and acoustic system design

## Description

Join us at Analog Devices as a Staff AI/ML Embedded ML/DSP Systems Engineer and lead the development of cutting-edge AI/ML systems that power real-time applications across industries like industrial automation, data centers, communications, and hardware design. At Analog Devices, we're committed to pushing the boundaries of innovation in Physical AI. Be at its forefront, working at the intersection of hardware and software to deliver scalable, production-grade solutions. This role offers the opportunity to drive technical strategy, mentor teams, and shape the future of AI/ML systems., * 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, and spatial filtering, with a clear path from MATLAB/Python prototypes to deployable fixed-point implementations
 * Contribute to audio ASIC system architecture definition - informing hardware spec decisions (precision, buffer sizes, DMA structures, NPU config) based on algorithmic and deployment requirements
 * Generate IP (patents) and represent the team's technical depth to OEM customers in automotive and hearable segments
 * Mentor engineers in deployment best practices, embedded optimization, and hardware-aware algorithm design


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