Machine Learning Engineer

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 About the Role Bose is seeking a Machine Learning Engineer with expertise in Efficient AI and Edge ML to join our team. In this role, you will design, train, optimize, and deploy machine learning models for next-generation audio products. You will work closely with embedded, DSP, compiler, hardware, and software engineers to bring AI capabilities to resource-constrained devices.

The ideal candidate has strong machine learning fundamentals, practical experience with efficient model development, and an understanding of Edge ML deployment constraints. Deep embedded systems expertise is not required. However, you should be able to understand deployment and integration feedback, identify model-related limitations, and quickly iterate on model architectures and training methods to improve accuracy, latency, memory usage, and power efficiency.

Responsibilities Design, train, evaluate, and optimize machine learning models for consumer audio products.

Apply Efficient ML techniques, including quantization, pruning, knowledge distillation, model compression, and efficient model architectures, to improve model efficiency while maintaining model quality.

Work closely with embedded, DSP, compiler, and hardware engineers to deploy and integrate machine learning models on resource-constrained platforms, including DSPs, NPUs, microcontrollers, and ARM-based systems.

Analyze deployment and integration feedback, identify model-related issues, and rapidly iterate on model architectures and training methodologies.

Adapt models to address platform constraints such as unsupported operators, data-type limitations, memory usage, latency, compute requirements, and power consumption.

Profile and evaluate model performance across accuracy, latency, memory footprint, model size, compute efficiency, and power usage.

Develop and maintain repeatable workflows for model training, conversion, compilation, simulation, validation, and performance analysis.

Collaborate across Machine Learning, embedded software, DSP, hardware, compiler, and product engineering teams to deliver production-ready AI solutions.

Evaluate emerging ML technologies and AI-assisted engineering tools that can improve model development, deployment, debugging, and optimization workflows.

Document experiments, technical findings, deployment learnings, and reusable engineering practices.

Mentor interns and co-op engineers.

Minimum Qualifications M.S. or Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, Machine Learning, or a related field.

Strong foundation in machine learning and deep learning, including model architecture design, training, evaluation, and optimization.

Working knowledge of Efficient ML techniques such as quantization, pruning, knowledge distillation, model compression, and efficient model architectures.

Understanding of how model optimization techniques affect model accuracy, latency, memory footprint, compute requirements, and deployment compatibility.

Proficiency with deep learning frameworks such as PyTorch, Tensor Flow, or similar.

Strong Python programming, debugging, and problem-solving skills.

Working knowledge of C/C++ and the ability to collaborate effectively with engineers working on embedded and production systems.

Understanding of Edge ML concepts and common resource constraints, including computation, memory, latency, and power.

Ability to analyze model behavior and troubleshoot issues across training, conversion, deployment, and integration workflows.

Strong communication and cross-functional collaboration skills.

Preferred Qualifications Hands-on experience with quantization-aware training, post-training quantization, mixed-precision inference, structured or unstructured pruning, operator replacement, or hardware-aware model optimization.

Experience adapting model architectures or training workflows in response to deployment and integration constraints.

Experience deploying…