DSP/ML Research Engineer Intern | Summer 2027

Bose Corporation•Framingham, MA

4d•$40 - $51

About The Position

Bose Corporation is seeking a Machine Learning/DSP Engineer Intern to join its research lab for a three-month summer position beginning in June 2027. The intern will help develop next-generation product experiences focused on hearing, music, and environmental sensing. The role is full-time at 40 hours per week and is based at a primary work location in Framingham, Massachusetts; hiring ranges may vary at other Bose locations. The intern will research, implement, and evaluate multimodal sensor-fusion methods; develop DSP and machine-learning algorithms for room environmental sensing; collect sensor data; and build machine-learning models, training data, and evaluation infrastructure. The work involves sensors including microphones, IMUs, and light-based systems.

Requirements

Currently pursuing an M.S. or Ph.D. in Electrical Engineering, Acoustic Engineering, Computer Science, or a related field.
Strong programming experience in Python and MATLAB.
Strong experience with TensorFlow or PyTorch.
Experience with camera and IMU data collection and signal processing.
Familiarity with machine learning, including deep-learning neural networks, memory-constrained models, and training-data development.
Familiarity with real-time processing applications.
Familiarity with acoustic modeling.
Experience with cross-group and cross-culture collaboration.
High creativity and quick problem-solving capabilities.

Nice To Haves

Demonstrated machine-learning engineering experience through an internship, co-op, class, or personal project.
Knowledge of embedded DSP.
Knowledge of C/C++ and bash scripting.
Music or acoustic experience and interests.

Responsibilities

Research, implement, and evaluate multimodal fusion methods for environmental sensing.
Develop and evaluate DSP and machine-learning algorithms focused on room environmental sensing.
Collect data using microphones, IMUs, and light-based systems.
Develop machine-learning models, training data, and evaluation infrastructure.