We are looking for a Gen AI Researcher for Audio to join our team and help develop next-generation voice synthesis models. You'll research and build deep learning systems that can generate expressive, natural-sounding speech from text or audio prompts, and collaborate with cross-functional teams to integrate your work into production-ready pipelines.
Key Responsibilities
- Research and develop state-of-the-art voice synthesis models (e.g., TTS, voice cloning, speech-to-speech).
- Build and fine-tune models using frameworks like PyTorch and HuggingFace.
- Design training pipelines and datasets for scalable voice model training.
- Explore techniques for emotional expressiveness, multilingual synthesis, and speaker adaptation.
- Work closely with product and creative teams to ensure models meet quality and production constraints.
- Stay on top of academic and industrial trends in speech synthesis and related fields.
Must Haves- Strong background in machine learning and deep learning, with focus on speech/audio.
- Hands-on experience with TTS, voice cloning, or related voice synthesis tasks.
- Proficiency with Python and PyTorch; experience with libraries like torchaudio, ESPnet, or similar.
- Experience training models at scale and working with large audio datasets.
- Familiarity with vocoders and transformer-based architectures.
- Strong problem-solving skills, ability to work autonomously in a remote-first environment.
Nice to Have- PhD degree in Computer Science/ Machine Learning and publications in top venues.
- Contributions to open-source speech research or participation in relevant benchmarks.
- Familiarity with adjacent areas like lip-syncing, audio-driven animation, or expressive speech control.
- Experience with voice datasets or proprietary pipelines.
