DSP/ML Research Engineer Co-op - Real-Time Audio R&D

Bose; Framingham (MA); On-site; USD 55,000 - 71,000; Full time

Benefits offered by this job

Bonus programs

Health & welfare benefits

401(k) plan

Employee discount

Job summary

Bose Corporation invites a Machine Learning/DSP Research Engineer Co-op for a 6-month Spring 2027 term in Framingham, MA. The role focuses on building next-generation product experiences through ML and DSP, with real-time demo capabilities on prototyping platforms.

The position requires an MS/PhD candidate in EE, Acoustic Engineering, CS, or related field, plus strong Python, MATLAB, and TensorFlow/PyTorch experience to work with sensor data including microphones and IMUs.

Qualifications

• Currently pursuing an M.S. or PhD in Electrical Engineering, Acoustic Engineering, Computer Science, or related field.
• Strong experience with Python and MATLAB.
• Experience with TensorFlow or PyTorch.
• Experience with camera and IMU data collection and signal processing.
• Familiar with ML, deep learning, memory-constrained models, and training data development.
• Familiar with real-time processing applications.
• Familiar with acoustic modeling.

Responsibilities

• Research, implement and evaluate multimodal fusion methods of sensors for environmental sensing.
• Develop and evaluate DSP and Machine Learning algorithms for room environmental sensing.
• Collect data using microphones, IMUs, and light-based systems.
• Develop ML models, training data, and evaluation infrastructure.
• Implement real-time demo-able experiences on prototyping platforms.

Skills

Python

MATLAB

TensorFlow

PyTorch

C/C++

Bash scripting

Real-time processing

Acoustic modeling

Education

MS or PhD in Electrical Engineering / Acoustic Engineering / Computer Science

Tools

TensorFlow

PyTorch

MATLAB

C/C++

Bash

Job description

Bose Corporation invites a Machine Learning/DSP Research Engineer Co-op for a 6-month Spring 2027 term in Framingham, MA. The role focuses on building next-generation product experiences through ML and DSP, with real-time demo capabilities on prototyping platforms.

The position requires an MS/PhD candidate in EE, Acoustic Engineering, CS, or related field, plus strong Python, MATLAB, and TensorFlow/PyTorch experience to work with sensor data including microphones and IMUs.