TTS Research Engineer

Representative responsibilities/duties will include but not limited to:

• Design and optimize text/NLP preprocessing pipelines with Deep Learning or Machine Learning methods, including Grapheme-to-phoneme (G2P) conversion for multilingual support; Text normalization; polyphone disambiguation; Prosody prediction and control
• Integrate language models (e.g., BERT, GPT variants) to improve contextual and semantic understanding for natural intonation
• Develop rule-based and neural solutions for emotion/style control in synthesized speech
• Build state-of-the-art acoustic models (e.g., VITS, VALL-E, CosyVoice) to map linguistic features to spectrograms or waveform parameters.
• Optimize neural vocoders (e.g., HiFi-GAN, MelGAN, Vocos) for high-fidelity, real-time speech synthesis
• Optimize inference latencies for both edge devices and cloud platforms
• Enhance robustness through noise suppression, speaker adaptation, and multilingual/cross-language/cross -gender voice cloning

Knowledge, skills, and qualifications

Education : Master in CS, AI, EE, Math, or related field.

Required/preferred skills :

• 2+ years of hands-on experience in TTS system development with deep expertise in both frontend and backend components
• Proficiency in C/C++ and Python, with mastery of ML frameworks (PyTorch, TensorFlow, etc)
• Some background in NLP techniques and/or speech signal processing is a plus
• Knowledge on transformer-based language models for prosody prediction
• Basic understanding of autoregressive / non-autoregressive acoustic models and neural vocoders
• Experience in optimizing models via quantization, pruning, or knowledge distillation
• Experience with ONNX Runtime, TensorRT, or TorchScript, etc
• Experience with zero-shot/one-shot/few-shot voice cloning or emotional TTS systems
• Skilled GPU/TPU cluster and grid user

CerenceAI champions diversity and innovation. We welcome applicants from all backgrounds.