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