Junior ML-AI Speech Enhancement and Denoising Software Engineer

Do you have a Master's degree?, * ML for Audio: Strong academic records and project experience developing ML models for audio applications (e.g., speech enhancement, separation, or classification).

• Data Handling: Familiarity with audio dataset curation, augmentation strategies, and defining robust evaluation metrics.
• ML Systems: Basic understanding of ML pipelines (training, validation, deployment) emphasizing code reproducibility.
• Good knowledge of deep learning architectures (CNNs, RNNs, Transformers, diffusion models, generative approaches) applied to acoustic data.
• Proficiency in Python or Matlab and ML frameworks (PyTorch, TensorFlow),
• Edge Optimization: Project experience optimizing models (quantization, pruning) for resource-constrained embedded platforms.
• Product Concepts: Conceptual understanding of integration challenges in real-time systems (latency, power, robustness).
• Research Output: Evidence of research contributions through academic publications or innovative thesis work in audio ML or acoustics.
• Continuous Learning: Demonstrated enthusiasm for staying current with the latest research in audio ML., * Advanced Degree: MSc or PhD in Computer Science, Electrical Engineering, Acoustics, Vibrations, Applied Math, or equivalent.
• 1+ years of relevant experience (including internship of academic projects)
• Experience developing and deploying ML models, specifically related to audio and speech.
• Track record of publications and/or patents if you have a PhD.
• Excellent communication and can-do mindset.

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