Deep Learning Scientist, Speech Synthesis

Key Responsibilities

• Train speech synthesis mel spectrogram and vocoder models
• Measure and benchmark model performance across use cases
• Maintain and enhance text to speech evaluation systems
• Analyze model accuracy and bias and recommend improvements
• Improve processes related to speech data preparation, augmentation, and filtering
• Develop and refine training datasets for speech models
• Characterize performance and quality metrics across different platforms
• Collaborate with cross functional teams to deliver new product features
• Participate in code development, design reviews, and test planning
• Identify issues, propose solutions, and contribute to continuous innovation

Required Qualifications

• Master’s degree or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Mathematics, Linguistics, or Computational Linguistics or equivalent experience
• Minimum of 5 years of relevant experience
• Strong programming skills in Python
• Solid understanding of programming fundamentals and software design
• Deep knowledge of machine learning and deep learning techniques including CNN, RNN, LSTM, and Transformers
• Experience applying deep learning to speech synthesis, large language models, and speech to speech translation
• Hands on experience with speech technologies such as speech synthesis and voice cloning
• Experience training speech models
• Proficiency with PyTorch deep learning frameworks
• Knowledge of speech signal processing techniques including FFT, MFCC, and mel spectrograms
• Familiarity with version control tools such as Git, Gerrit, or GitLab
• Strong collaboration and communication skills in a matrixed environment

Preferred Qualifications

• Fluency in one or more languages such as Spanish, Mandarin, German, Japanese, Russian, French, Arabic, Hindi, Korean, Italian, or Portuguese
• Experience with multilingual or code switched text to speech systems
• Experience with voice cloning and cross lingual voice cloning
• Knowledge of text normalization and inverse text normalization using neural networks or WFST
• Experience working with grapheme to phoneme systems for multiple languages
• Interest in linguistics, phonetics, and language technologies
• Strong C plus plus programming skills
• Familiarity with GPU technologies such as CUDA, cuDNN, or TensorRT
• Experience deploying machine learning models to cloud, data center, or embedded systems