Deep Learning Scientist Speech Synthesis

We are seeking a Deep Learning Scientist Speech Synthesis to support the development of next-generation speech AI technologies. This role focuses on training and optimizing speech models, improving model performance, and solving complex machine learning challenges related to speech applications., * Train and optimize speech synthesis models, including mel spectrogram and vocoder models.

• Analyze training metrics, validation losses, and model performance to identify root causes of model issues and recommend improvements.
• Benchmark and optimize speech models across multiple use cases.
• Improve speech data preparation, augmentation, filtering, and dataset quality.
• Develop and refine high-quality training datasets for speech AI models.
• Measure and characterize model accuracy, quality, and bias.
• Collaborate with cross-functional teams to develop and deliver new speech AI features.
• Participate in software development, design reviews, testing, and code reviews.
• Troubleshoot technical issues and contribute to continuous model improvements.

The ideal candidate has strong experience in speech synthesis (Text-to-Speech) or Speech-to-Text, deep learning, and Python development. Success in this role requires the ability to analyze model behavior, diagnose training issues, and improve model performance not just collect or evaluate data., * Master's degree or Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, Linguistics, Computational Linguistics, or a related field (or equivalent experience).

• 3+ years of relevant industry experience.
• Strong Python programming skills.
• Strong understanding of machine learning and deep learning concepts.
• Experience with Text-to-Speech (TTS), Speech Synthesis, or Speech-to-Text (STT) technologies.
• Hands-on experience training deep learning models using PyTorch.
• Ability to analyze training behavior, validation losses, and model performance to troubleshoot and improve machine learning models.
• Knowledge of speech signal processing concepts, including FFT, MFCC, and mel spectrograms.
• Strong understanding of software development fundamentals.
• Experience using version control systems such as Git, Gerrit, or GitLab.
• Excellent communication and collaboration skills., * Experience with deep learning architectures such as CNNs, RNNs, LSTMs, and Transformers.
• Experience with voice cloning or multilingual speech systems.
• Knowledge of text normalization (TN), inverse text normalization (ITN), or grapheme-to-phoneme (G2P) systems.
• Fluency in one or more languages such as Spanish, Mandarin, German, Japanese, Russian, French, Arabic, Hindi, Korean, Italian, or Portuguese.
• Interest in linguistics, phonetics, and speech technologies.
• Strong C++ programming skills.
• Familiarity with GPU technologies such as CUDA, cuDNN, or TensorRT.
• Experience deploying machine learning models to cloud, data center, or embedded environments.