
Machine Learning / AI Engineer
Required skills
- Machine Learning
- AI
- Python
- PyTorch
- TensorFlow
- model training
- model evaluation
- model optimization
- latency
- memory use
Nice to have
- quantization
- pruning
- model compression
- Edge ML
- embedded AI
- limited computing resources
Role summary
Bose Corporation is seeking a Machine Learning / AI Engineer to develop intelligent capabilities for next-generation audio experiences. This role focuses on building and optimizing machine learning models for efficient on-device performance, particularly for products with limited memory and computing power. Responsibilities include model development, training, evaluation, and collaboration with cross-functional engineering teams to deploy AI features. The ideal candidate will have a strong foundation in ML/DL, proficiency in Python and frameworks like PyTorch or TensorFlow, and experience with model efficiency techniques. Experience with Edge ML, C/C++, and audio-specific ML applications is a plus.
At Bose Corporation, we believe sound is the most powerful force on earth — and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.
Job Description
Build AI that people can hear and experience.
At Bose, we believe sound is the most powerful force on earth. As a Machine Learning / AI Engineer, you’ll help bring that belief to life - developing intelligent capabilities for the next generation of audio experiences.
If you enjoy experimenting with models, solving practical problems, and turning ideas into working technology, this is an opportunity to grow your skills while contributing to products and technology that people use every day.
About the Role
You’ll help design, train, test, and optimize machine learning models, with a focus on making AI work efficiently on devices with limited memory and computing power. This work brings together Efficient AI and Edge ML: building models that deliver strong results within the constraints of real products.
You’ll collaborate with engineers across machine learning, software, embedded systems, and hardware. Along the way, you’ll deepen your technical expertise, contribute your ideas, and learn how models move from experimentation toward deployment.
What You’ll Do
- Build and experiment. Develop, train, and evaluate machine learning models for consumer audio applications.
- Make models more efficient. Explore ways to improve accuracy while reducing latency, memory use, and computational demands.
- Connect models to products. Partner with software, embedded, and hardware engineers to help bring AI capabilities onto devices.
- Investigate and improve. Analyze results, debug issues, and use testing to guide your next experiment.
- Learn and contribute. Explore emerging ML techniques, share what you discover, and bring fresh ideas to the team.
What You’ll Bring
- A bachelor’s or advanced degree in Computer Science, Electrical Engineering, Machine Learning, or a related technical field.
- 3+ years of relevant machine learning or AI experience, which may include applicable research or substantial project work.
- A solid foundation in machine learning and deep learning, with hands-on experience building and evaluating models.
- Proficiency in Python and experience with a framework such as PyTorch or TensorFlow.
- Experience training, evaluating, and debugging models—and curiosity about how to make them better.
- An analytical approach to problem-solving, a willingness to ask questions, and enthusiasm for working with others.
Experience That Can Help You Stand Out
You don’t need experience in every area below. We’re interested in the strengths you bring and what you’re excited to learn.
- Techniques such as quantization, pruning, or model compression.
- Edge ML, embedded AI, or deploying models on devices with limited computing resources.
- C/C++ or deployment tools such as ONNX or TensorFlow Lite.
- Audio ML, speech processing, acoustic sensing, or related applications.
- Emerging AI technologies and AI-assisted engineering tools.
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