Audio AI Research Scientist — Audio Source Separation

Bose Corporation, U.S.A•Framingham, MA

About The Position

Bose Corporation is seeking a Research Scientist to develop novel AI-powered audio processing algorithms, with a focus on audio source separation. The algorithms must operate in real time on physical devices and support applications such as dynamic awareness, hearing augmentation, agentic perception, voice pickup, and content enhancement. The role involves collaborating with experts in machine learning, digital signal processing, software engineering, and psychoacoustics to prototype and implement new algorithms. The researcher will develop algorithms that isolate, extract, or suppress individual sound sources from microphone recordings, sometimes using additional sensor signals. Work includes researching, prototyping, training, and evaluating methods for speech and other real-world sounds, and contributing to real-time demonstrations and product platforms. The position is based in a company that develops audio technology and experiences.

Requirements

Graduate degree (MS or PhD) in machine learning, electrical engineering, computer science, or a related field, or equivalent research experience.
Deep hands-on experience in audio source separation, target sound extraction, speech enhancement, or related neural audio processing.
Ability to tackle complex, open-ended problems and experiment with different approaches.
Strong knowledge of audio DSP, time-frequency methods, and microphone array or spatial signal processing.
Experience training and evaluating audio models in PyTorch.
Proficiency in Python.
Understanding of objective and perceptual evaluation methods and the tradeoffs involved in real-time low-power inference.
Strong communication skills.

Nice To Haves

Familiarity with C/C++.
Experience with multimodal or sensor-conditioned separation, ONNX/TFLite, or embedded deployment.
Publications at conferences such as ICASSP, Interspeech, NeurIPS, or similar.

Responsibilities

Research, prototype, train, and evaluate novel source separation and extraction algorithms for speech and other real-world sounds.
Develop approaches for single- and multi-microphone recordings, including spatial cues and optional auxiliary sensors.
Investigate robust separation in challenging conditions, including overlapping talkers, noise, reverberation, and motion.
Evaluate perceptual quality, separation performance, generalization, latency, memory, and computational requirements.
Collaborate with DSP and embedded software teams to deploy models in real-time demonstrations and product platforms.
Contribute to patents and conference papers.