Join Bose: Elevate Sound with Passion and Precision

Experience the power of sound with Bose. Remember the first notes of your favorite song or the warm "hello" from a loved one? At Bose, we believe sound is the most potent force on Earth, and we are dedicated to perfecting it for nearly 60 years. Our engineering team is a diverse group of skilled professionals, united by a passion for creating products that deliver transformative audio experiences.

Job Opportunity: Senior Audio Machine Learning Operations Engineer

About Us

At Bose, we craft high-quality products that astonish. We achieve this by obsessing over the details to create amazing user experiences and high-performance technologies. Our innovative team excels in pushing the boundaries of machine learning-powered voice and audio processing. We are excited to find a talented MLOps Engineer to join us on this groundbreaking journey.

Job Description

We seek a skilled MLOps Engineer responsible for developing and deploying machine learning models across various platforms, including embedded, mobile, and desktop. The ideal candidate will have a robust background in Python and experience with deep learning frameworks such as PyTorch and TensorFlow. Familiarity with TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and embedded programming is essential. A solid understanding of traditional signal processing pipelines related to voice pickup and multi-channel audio reproduction is also vital.

Responsibilities

• Collaborate with cross-functional teams, including ML research, product DSP, and product software/firmware, to understand project requirements and drive ML algorithms into products.
• Fine-tune and develop machine learning models for production deployment, considering use-case requirements, firmware, and integration into DSP pipelines.
• Design and implement data collections to feed existing data pipelines for model training and evaluation.
• Provide insights on systems-level DSP architecture for efficient audio ML model deployment.
• Build platform-agnostic infrastructure to validate and deploy models to target hardware.
• Monitor model performance on deployed targets and address any issues.
• Stay updated on the latest advancements in machine learning, embedded systems, and signal processing to incorporate relevant technologies and techniques into our workflows.

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, or a related field.
• Strong experience with Python, including audio and ML frameworks.
• System-level understanding of voice-processing DSP architectures. Experience in developing with MATLAB, with bonus points for Bose-specific MATLAB toolboxes.
• Proven experience in developing and deploying machine learning models using Python, PyTorch, and TensorFlow.
• Strong understanding of machine learning concepts and techniques, including deep learning.
• Experience with TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and embedded programming.
• Experience with porting ML models to embedded devices, targeting hearables, especially Qualcomm and Tensilica HiFi DSPs and AI accelerators.
• Excellent problem-solving skills and ability to work effectively in a fast-paced, collaborative environment.
• Strong communication skills to explain complex technical concepts to non-technical stakeholders.

Benefits

• Competitive salary and benefits package.
• Opportunities to work with cutting-edge technologies and make a significant impact in a rapidly evolving industry.
• Collaborative and dynamic work environment with professional growth and development opportunities.

If you are passionate about leveraging machine learning to drive innovation and solve real-world problems, we'd love to hear from you! Please submit your resume and cover letter detailing your relevant experience and why you're interested in joining our team.

Bose is an equal opportunity employer committed to inclusion and diversity. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status, or any other legally protected characteristics. For additional information, please review the and its . Pay transparency details are