ml engineer for voice AI

The company develops generative audio and voice transformation technologies.

• Develop, train, and fine-tune machine learning models for voice synthesis (TTS) and AI voice conversion;
• Design, build, and maintain robust ML pipelines from data preparation to production;
• Optimize audio processing models for streaming and real-time inference to achieve ultra-low latency;
• Turn research-grade models into reliable production web services and REST APIs;
• Analyze and improve the quality of synthesized audio, ensuring natural sound and high fidelity;
• Collaborate with the team to integrate ML models into the final user-facing product.

• Strong foundational knowledge of Python;
• Proven hands-on experience and strong skills in Voice Synthesis (TTS);
• Deep understanding of AI Voice Conversion algorithms and audio signal processing;
• Experience building and deploying complete ML pipelines;
• Practical experience with streaming optimization and real-time audio ML serving;
• Hands-on experience with modern deep learning frameworks such as PyTorch, NeMo, and HuggingFace;
• Ability to write clean, production-ready code beyond Jupyter Notebooks;
• English — B2+;
• Nice to have: experience with voice/audio models as part of a broader ML stack, background in Natural Language Processing (NLP) or Automatic Speech Recognition (ASR), experience with backend frameworks such as FastAPI and containerization with Docker, familiarity with audio libraries such as PyDub and Librosa, experience with MLOps practices and tracking tools such as Weights & Biases.

• No conditions specified