➕ About the project: We are an innovative product company developing cutting-edge generative audio and voice transformation technologies. We do not rely on formal labels (Junior/Mid/Senior) as they are often misleading indicators of actual skill. Instead, we are looking for a candidate based on their real, hands-on confidence in voice synthesis and Python, regardless of their official "grade."

💼 Responsibilities:

— 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 seamlessly integrate ML models into the final user-facing product

🧠 Requirements:

— Strong, foundational knowledge of Python — this is an absolute prerequisite no matter what your level is — 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 (PyTorch, NeMo, HuggingFace, etc.) — Ability to write clean, production-ready code (moving beyond Jupyter Notebooks) — English — B2+

💫 Nice to have:

— Experience working with voice/audio models as part of a broader, more complex ML stack — Background in Natural Language Processing (NLP) or Automatic Speech Recognition (ASR) — Experience with backend frameworks (e.g., FastAPI) and containerization (Docker) — Familiarity with audio libraries like PyDub, Librosa, or similar — Experience with MLOps practices and tracking tools (Weights & Biases, etc.)