About Analog Devices

Analog Devices, Inc. (NASDAQ: 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 and on LinkedIn and Twitter (X).

Staff Embedded ML/DSP Systems Engineer (Audio Engineering)

About the Role:

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.

Key Responsibilities

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, 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

Required Qualifications:

• 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 (Tensor Flow/TFLite, Py Torch/ONNX)

• Experience with low-level profiling tools, instruction set architectures, and memory optimization for embedded AI inference

Preferred Qualifications:

• 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

Why You’ll Love Working at ADI:

At Analog Devices, you'll be part of a collaborative and innovative team that's shaping the future of technology. We offer a supportive environment focused on professional growth, competitive compensation and benefits, work-life balance, and the opportunity to work on cutting-edge projects that make a real impact on the world.
Your expertise will shape the future of technology, and you’ll be supported by a culture that values continuous advancement and professional growth. Join us and help create the technologies that bridge the physical and digital worlds, making a tangible difference in how people live, work, and connect.

*For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S.

Department of Commerce:

• Bureau of Industry and Security and/or the U.S.

Department of State:

• Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.*

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.

Job Req Type: Experienced

Required Travel: Yes, 10% of the time

Shift Type: 1st Shift/Days