Machine Learning Engineer

Bose Corporation is seeking a Machine Learning Engineer to develop and deploy efficient machine learning models for next-generation consumer audio products. The role focuses on optimizing models for resource-constrained devices, integrating them with cross-functional engineering teams, and improving model performance based on deployment feedback.

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