Senior ML/AI Speech Enhancement and Denoising SW Eng...

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  and on LinkedIn and Twitter (X).

Senior ML/AI Speech Enhancement and Denoising Software Engineer

Location

Belgium (Hybrid / Onsite)

Summary

We are seeking an experienced Senior ML/AI Audio Denoising Software Engineer to contribute to the development of advanced deep learning models for next-generation audio and speech systems on hearables (earbuds, headphones, head mounted devices) and wearables. You will participate to the full lifecycle of ML-based solutions — from research and prototyping to optimization and deployment on embedded hardware with customer support — while collaborating with cross-functional teams in AI, DSP, acoustics, hardware, and systems.

Responsibilities

• Research and development of state-of-the-art ML models for audio applications: speech enhancement, separation, echo cancellation, spatial audio, and classification.
• Help drive the creation and curation of large-scale audio datasets, data augmentation strategies, and evaluation metrics.
• Optimize and update ML pipelines for training, validation, and deployment with scalability and reproducibility in mind.
• Optimize models for edge/embedded platforms (quantization, pruning, distillation, hardware accelerators).
• Collaborate with acoustics and hardware teams to integrate ML algorithms into products, considering latency, power, and robustness.
• Mentor more junior engineers, review code, and contribute to a culture of technical excellence.
• Publish and patent innovative methods in ML for audio, speech enhancement, hearing augmentation and acoustics.
• Stay at the forefront of research in audio ML, evaluating new architectures and techniques.

Minimum Qualifications

• PhD (or Master's with industry experience) in Machine Learning, Computer Science, Electrical Engineering, or related field – in an audio related context.
• 5+ years of experience developing and deploying ML models, with at least 3+ years specifically in audio/speech.
• Proven expertise in deep learning architectures (CNNs, RNNs, Transformers, diffusion models, generative approaches) applied to acoustic data.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow), including model training and optimization.
• Hands-on experience with ML deployment on embedded/mobile platforms (C/C++, ONNX, TensorRT, TFLite, CoreML).
• Track record of publications, patents, or productized audio ML solutions.
• Excellent communication and ability to drive cross-functional collaboration.

Preferred Qualifications

• Experience working in small teams or start-ups.
• Good understanding of ANC principles
• Expertise in large-scale training (multi-GPU, distributed training, cloud pipelines).
• Knowledge of psychoacoustics and perceptual evaluation methods.
• Strong connections with academic or industrial research communities.

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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.