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Premium audio solutions for home, car, and portable use

Chief Financial Officer

SVP and Chief Engineering Officer

Audio Amplifier Market Size to Hit USD 7.06 Billion by 2035 with a 4.95% CAGR

Automotive Premium Audio Systems Market to Reach USD 6.25 Bn: Growth Drivers, Innovation, and Competitive Outlook

Wireless Audio Devices Industry Poised for $70.9 Billion Market Boom by 2029

USB Cable Market Projected to Surpass $69.86 Billion with a 20.24% CAGR by 2031

Agentic AI Engineering Co-op – Active Noise Cancellation

Bose Corporation is hiring an Agentic AI Engineering Co-op to explore how AI agents can support engineers working on active sound management technologies. The co-op will document engineering workflows, modernize tools, connect engineering functions to AI agents, evaluate and safeguard agent outputs, and demonstrate an agent-assisted workflow.

Agentic AI Engineering Co-op – Active Noise Cancellation | Fall 2027

Audio AI Research Scientist — Audio Source Separation

Bose Corporation is developing real-time AI-powered audio processing algorithms for physical devices. The Audio AI Research Scientist will research, prototype, train, and evaluate source separation and extraction algorithms, then collaborate with DSP and embedded software teams on deployment and research contributions.

Machine Learning Engineer

Bose Corporation is hiring a Machine Learning Engineer to develop efficient machine learning models for next-generation audio products. The role focuses on optimizing and deploying models on resource-constrained platforms in collaboration with embedded, DSP, compiler, hardware, and software teams.

Audio AI Research Scientist - Audio Understanding

Bose Corporation develops audio technology and experiences. The Audio AI Research Scientist develops and deploys real-time audio understanding algorithms for devices, including sound event detection, acoustic scene understanding, anomaly detection, and multimodal context understanding.

Audio AI Research Scientist — Audio Understanding

Research Engineer — Agentic Audio ML Workflows

Bose Corporation develops AI-powered audio processing algorithms designed to run in real time on physical devices. The Research Engineer will build agentic tools and automation to orchestrate audio ML training, evaluation, experimentation, and reporting across on-premises and cloud GPU infrastructure.

DSP/ML Research Engineer Intern

Bose Corporation is hiring a DSP/ML Research Engineer Intern for its research lab. The intern will develop and evaluate multimodal sensor-fusion methods and DSP and machine learning algorithms for environmental and room sensing, and build models, training data, and evaluation infrastructure.

DSP/ML Research Engineer Intern | Summer 2027

DSP/ML Research Engineer Co-op

Bose Corporation is hiring a DSP/ML Research Engineer Co-op to develop next-generation audio and environmental sensing experiences. The role focuses on multimodal sensor fusion, DSP and machine learning algorithms, sensor data collection, model and evaluation development, and real-time demonstrations.

DSP/ML Research Engineer Co-op | Spring 2027

Principal Audio DSP/ML Engineer - Audio Technology

Bose Corporation is hiring a Principal Audio DSP/ML Engineer to invent, develop, and productize advanced audio technologies for consumer devices. The role combines signal processing, machine learning, and systems engineering, taking algorithms from research through real-time deployment on production hardware.

Principal Audio DSP/ML Engineer- Audio Technology

Embedded Audio ML Engineer Co-op

Bose Corporation develops audio technology and consumer audio products. The Embedded Audio ML Engineer Co-op will help deploy and optimize deep learning algorithms for resource-constrained devices, supporting new machine-learning-powered audio features across Bose products.

Machine Learning / AI Engineer

Bose Corporation is hiring a Machine Learning / AI Engineer to develop intelligent capabilities for consumer audio products. The role focuses on designing, training, evaluating, and optimizing models for efficient performance on resource-constrained devices, in collaboration with software, embedded systems, and hardware engineers.