Ms.c. in Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field 5+ years of industry experience in applied deep learning, data science, or a related field Hands-on experience working with physical/real-world signal data Strong hands-on experience with Python, PyTorch and SQL for large-scale signal/waveform data analysis and pipeline development Hands-on experience with the full deep learning experimentation cycle: problem definition, data collection, statistical analysis, and conclusion-driven iteration Proven ability to analyze model failures and translate findings into concrete improvements Strong analytical thinking and ability to independently define and drive research directions Excellent cross-functional communication skills - ability to work effectively with other Deep Learning and Data Engineers Experience with model explainability and interpretability methods - a strong advantage Experience with continual or online learning - a strong advantage Experience with data-efficient training strategies - a strong advantage Experience with applied speech, audio, or signal processing deep learning systems Familiarity with data quality frameworks, monitoring pipelines, and data validation at scale Strong statistical foundation - hypothesis testing, uncertainty quantification, evaluation metrics design Ph.D. in Computer Science, Electrical Engineering, Computational Biology/Neuroscience, Mathematics, Statistics, or a related field
