Remote Edge AI Engineer - Audio & Embedded ML

Bose Corporation; Massachusetts; Remote; USD 115,000 - 158,000; Full time

Benefits offered by this job

Health benefits

401(k) plan

Employee discount

Wellbeing perks

Job summary

Bose Corporation is seeking a Machine Learning / AI Engineer to design, train, test, and optimize ML models for consumer audio devices and edge applications. You will collaborate across ML, software, embedded systems, and hardware teams to deploy efficient AI on devices.

Ideal candidates have a strong ML/dl background, hands-on model building in Python with PyTorch or TensorFlow, and a passion for advancing on-device AI under memory and compute constraints.

Qualifications

• Bachelor’s or advanced degree in CS, EE, ML, or related field.
• 3+ years of ML/AI experience, including research or project work.
• Solid foundation in ML and deep learning with hands-on model building.
• Proficiency in Python with PyTorch or TensorFlow.
• Experience training, evaluating, and debugging models.
• Strong analytical, problem‑solving, and collaborative skills.

Responsibilities

• Build, train, and evaluate ML models for audio/embedded applications.
• Improve model accuracy while reducing latency and memory use.
• Collaborate with software, embedded, and hardware teams to deploy AI.
• Analyze results, debug issues, and guide experiments.
• Learn new ML techniques and share findings with the team.

Skills

Bachelor's degree in CS/EE/ML

3+ years ML experience

Python

PyTorch or TensorFlow

Model evaluation & debugging

Analytical problem-solving

Team collaboration

Education

Bachelor's or advanced degree in CS/EE/ML or related

Tools

PyTorch

TensorFlow

ONNX

TensorFlow Lite

Job description

Bose Corporation is seeking a Machine Learning / AI Engineer to design, train, test, and optimize ML models for consumer audio devices and edge applications. You will collaborate across ML, software, embedded systems, and hardware teams to deploy efficient AI on devices.

Ideal candidates have a strong ML/dl background, hands-on model building in Python with PyTorch or TensorFlow, and a passion for advancing on-device AI under memory and compute constraints.