About the Job: We are looking for a skilled MLOps Engineer who will be responsible for developing and deploying machine learning models efficiently and effectively to a variety of embedded, mobile, and desktop platforms. The ideal candidate will have a strong background in Python, along with experience in deep learning frameworks such as PyTorch and/or TensorFlow. Additionally, experience with technologies such as TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and embedded programming is essential for this role. Some understanding of traditional signal processing pipelines as they pertain to voice pickup and/or multichannel audio reproduction is also important to this role.

Responsibilities:

• Collaborate with cross-functional teams across 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 requirements including use-case (latency, size, opset), firmware (format, bit-depth, quantization schema), and integration into DSP pipeline (spectral transform, gain staging).
• Design and implement data collections designed to feed existing data pipelines for model training and evaluation.

• Provide insight on systems level DSP architecture for efficient and optimal deployment of audio ML models.
• Build platform-agnostic infrastructure to validate and deploy models to target hardware.
• Monitor model performance on deployed target and address any issues that arise.

• Stay updated on the latest advancements in machine learning, embedded systems, and signal processing, and 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 developing in MATLAB. Bonus points for Experience with 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/or Tensilica HiFi DSP’s and AI accelerators.
• Excellent problem-solving skills and the ability to work effectively in a fast-paced, collaborative environment.
• Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.

If you're 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.