Embedded Audio ML Engineer Co-op

Bose Corporation•Framingham, MA

23h•$40 - $51•Hybrid

About The Position

Bose Corporation is seeking students to participate in our Co-op Program who share our belief that sound is power. During the six-month co-op, students will have the chance to apply classroom learning in a hands-on work environment. Co-op participants will network across the business to gain diverse perspectives within Bose Corporation. Additionally, co-ops will connect with other students and colleagues, building valuable professional relationships for the future. Spring Co-op: Candidates must be available to work full-time (40 hours per week) in a hybrid, in-office format from January through July, 2027. No relocation assistance is available. At Bose, we use machine learning across a diverse set of problems ranging from speech enhancement (e.g., SpeechClarity) to audio perception. As an Embedded Audio ML Engineer Co-op at Bose, you will help deploy and optimize our next generation deep learning algorithms for resource-constrained devices.

Requirements

Currently pursuing a M.S. or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, or a related field
Strong proficiency in C/C++ for embedded systems.
Proficiency in Python for model conversion, evaluation, or tooling workflows.
Strong experience implementing deep neural networks with PyTorch, TensorFlow, or similar frameworks
Proven cross-group and cross-culture collaboration skills
Creative problem-solver with demonstrated ability to work independently

Nice To Haves

Prior internship or work experience as a Software/ML engineer
Experience deploying ML models on edge or resource‑constrained devices (MCU/DSP/NPU).
Solid background in audio deep learning and digital signal processing

Responsibilities

Build embedded software prototypes to demonstrate new ML-powered audio features on our wearables
Design, implement, deploy and optimize deep learning models, data structure and algorithms on resource-constrained devices.
Research, design and implement novel methods for efficient deep learning (e.g., quantization, pruning, NAS, knowledge distillation) with a focus on audio-related problems.
Train and evaluate deep learning models to enable new experiences on our headphones, earbuds, soundbars, portable speakers and other consumer audio devices.
Evaluate neural network hardware accelerators for low-power wearable devices, including working with various vendors’ custom toolchains, software and hardware and informing leadership teams about tradeoffs.