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
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.
- 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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