Principal Audio DSP/ML Engineer- Audio Technology

Bose Corporation•Framingham, MA

2d•$216,000 - $297,000•Remote

About The Position

We are seeking a Principal Audio DSP / Machine Learning Applied Scientist to invent, develop, and productize advanced audio technologies. In this role, you will combine signal processing, machine learning, and systems engineering to deliver high-quality audio solutions for real-world consumer devices. You will work across the full pipeline—from algorithm research and deployment to optimized, real-time deployment on production hardware. Bose has a strong history of combining creative thinking with cutting-edge technology in the audio domain. We are looking for candidates passionate about machine learning and audio to help us shape the next chapter in the future of Bose!

Requirements

Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or related field.
Hands-on experience with machine learning applied to audio or time-series data.
Strong fundamentals in digital signal processing (sampling, filtering, FFTs, spectral analysis).
Experience with Python and/or MATLAB for research and prototyping.
Solid understanding of audio systems and acoustics.
Strong communication skills.

Nice To Haves

MS or PhD in a relevant technical field.
Deep practical knowledge of: Applied ML frameworks such as PyTorch/ONNX and/or TensorFlow/TFLite.
Audio DSP (sampling, filtering, FFTs, spectral analysis) using tools such as Python, Matlab and/or C/C++.
Experience with speech or audio ML tasks, such as: Audio source separation / speech enhancement, Echo reduction, Microphone array signal processing
Strong software engineering practices (debugging, profiling, version control).
Proficiency in C and/or C++ for performance-critical systems.
Publications, patents, or demonstrated innovation in DSP or ML.

Responsibilities

Design and develop audio signal processing and machine learning algorithms for speech and audio applications such as noise suppression, echo cancellation, beamforming, spatial audio, and audio enhancement
Apply primarily modern ML/deep learning approaches augmented by classical DSP techniques to audio and time-series data.
Investigate new modeling approaches and translate research ideas into practical, scalable solutions that will be experienced by millions of consumers.