Audio ML Systems Engineer

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General Summary:

Job Overview

We are looking for a Senior Audio ML Systems Engineer to bridge the gap between traditional digital signal processing (DSP) and modern machine learning (ML) workflows. In this role, you will lead the development, tuning, and deployment of advanced speech and audio solutions for next-generation mobile devices.

You will work across the entire audio system lifecycle - from building robust ML training workflows and data augmentation pipelines to establishing rigorous validation frameworks. By combining your deep foundation in signal processing with hands-on ML model deployment and optimization, you will drive the transition of complex audio algorithms from research and development into highly optimized, commercial products.

Responsibilities

• Design and execute end-to-end ML workflows for audio processing, covering data preparation, model training, and performance tuning.

• Integrate digital signal processing (DSP) techniques with ML model architectures to optimize speech enhancement and noise suppression systems.

• Develop custom data augmentation strategies to ensure algorithms are robust across diverse and challenging real-world acoustic environments.

• Define and implement rigorous validation frameworks and testing protocols to evaluate audio quality, system stability, and latency.

• Analyze training results and system-level metrics to guide continuous improvements in model accuracy and computational efficiency.

• Collaborate directly with NPU architecture, firmware, acoustic hardware, and algorithms teams to transition audio systems into commercial mobile platforms.

Minimum Qualifications

• PhD or Master's degree in Electrical Engineering, Computer Science, Signal Processing, Acoustics, or Applied Mathematics.

• 3+ years of hands-on experience in audio ML, speech processing, or DSP system development.

• Solid foundation in digital signal processing (DSP) principles (e.g., filter design, frequency domain analysis) and audio systems.

• Hands-on experience with Python and industry-standard machine learning frameworks (e.g., PyTorch, TensorFlow, ONNX).

• Demonstrated experience with ML workflows, including model training, evaluation, and data pipeline management.

Preferred Qualifications

• Proven experience developing robust validation frameworks and utilizing objective audio quality metrics (e.g., PESQ, POLQA).

• Experience deploying and optimizing ML models on embedded devices, mobile NPUs, or edge hardware (e.g., TensorRT, ONNX Runtime).

• Lab experience with acoustic measurement, tuning, or system evaluation tools (e.g., HEAD acoustics).

• Familiarity with global voice and audio standards (e.g., 3GPP, ITU-T).

• Comfortable working in a fast-paced R&D environment with both research innovation and commercial product delivery goals.

Minimum Qualifications:

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Systems Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.

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