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Niche audio-ML specialization reduces competition, but mid-level (2–6 years) experience increases applicant density.
Specialized speech/audio ML expertise limits transferability across industries.
Mandatory 2+ years audio/speech experience, specific ML models, and required C/C++ and Python skills.
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Design, develop, test, and deploy speech enhancement products using AI/ML frameworks like PyTorch and TensorFlow.
Enhance existing speech enhancement models focusing on improving performance, reducing latency, and lowering computational requirements.
Adapt models to support multi-channel audio inputs for product improvement.
2-6 years of work experience, with at least 2 years in Audio/Speech domain.
Strong understanding of signal processing, machine learning, and deep learning techniques including CNNs, RNNs, LSTMs, Transformers for speech processing.
Proficient in programming with C/C++ and Python; experienced with AI/ML frameworks (PyTorch, TensorFlow).
Bachelor’s or Master’s degree in AI/ML, Computer Science Engineering, or Electronics and Communication Engineering.
Capable of independently driving speech enhancement product development and adapting AI models for multi-channel audio scenarios.
Experienced with deploying practical audio AI solutions balancing performance, latency, and compute efficiency.
Solid background in both theoretical (linear algebra, optimization, statistics) and applied deep learning techniques tailored to speech/audio processing.