Embedded Audio ML Engineer — Co-op

Bose Corporation, U.S.A; Framingham (MA); Hybrid; USD 55,000 - 70,000; Part time

Benefits offered by this job

Health insurance

401(k) plan

Employee discount

Job summary

Bose Corporation is offering a six‑month co‑op program based in Framingham, MA, where students will apply classroom learning in a hands‑on environment. You’ll deploy and optimize deep learning for resource‑constrained devices in a hybrid in‑office setting from January to July 2027.

The role focuses on embedding ML into audio wearables, with opportunities to work on SpeechClarity and other ML problems across headphones, earbuds, and soundbars.

Qualifications

• Currently pursuing a MS or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field.
• 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.

Responsibilities

• Build embedded software prototypes to demonstrate new ML‑powered audio features on wearables.
• Design, implement, deploy and optimize deep learning models on resource‑constrained devices.
• Research, design and implement methods for efficient deep learning (quantization, pruning, NAS, distillation) for audio problems.
• Train and evaluate deep learning models to enable experiences on headphones, earbuds, soundbars, and speakers.
• Evaluate neural network hardware accelerators for low‑power wearable devices and advise on tradeoffs.

Skills

C/C++

Python

PyTorch

TensorFlow

Collaboration

Independent work

Education

MS or PhD in CS/EE/ML

Job description

Bose Corporation is offering a six‑month co‑op program based in Framingham, MA, where students will apply classroom learning in a hands‑on environment. You’ll deploy and optimize deep learning for resource‑constrained devices in a hybrid in‑office setting from January to July 2027.

The role focuses on embedding ML into audio wearables, with opportunities to work on SpeechClarity and other ML problems across headphones, earbuds, and soundbars.