Role: Machine Learning Engineer (Rust / Python / Voice AI)

Location: Paris

Job type: Full-time

Work setup: 2-3 days remote per week

Start: ASAP

Job offer

About pyannoteAI

pyannoteAI is pioneering Speaker Intelligence AI, transforming how AI processes and understands spoken language. Our speaker diarization technology distinguishes speakers with unmatched precision, regardless of the spoken language, making AI understand not just what is said, but who said it and when.

Founded by voice AI experts with 10+ years in the industry (ex-CNRS research scientists), we've built the 9th most downloaded open-source model on HuggingFace with 52 million monthly downloads and over 140,000 users worldwide. After raising €8M from leading international VCs (Crane Venture Partners, Serena, and angels from HuggingFace and OpenAI), we're now scaling our enterprise platform.

From meeting transcription and call center analytics to video dubbing and voice agents, pyannoteAI powers the next generation of voice-enabled applications across industries that depend on understanding who speaks and when.

🧵 Your role

As a Machine Learning Engineer at pyannoteAI, you'll bridge cutting-edge research and production systems, transforming state-of-the-art speaker diarization models into scalable, real-time voice processing infrastructure. Working directly with our research scientists and within the Tech team, you'll write production code in Python and Rust, optimize for low-latency inference, and build the ML infrastructure that powers the leading diarization model in the VoiceAI space.

You'll:

• Design, implement and deploy ML models, particularly in the Audio/Voice AI domain (e.g., speaker diarization, speech separation, speech recognition, etc).
• Develop products/services in Rust/Python to support model training and inference in both streaming and batch pipelines.
• Work with ML frameworks like PyTorch / ONNX
• Build and maintain containerized environments (using Docker) for model training/inference, testing, and CI/CD pipelines.
• Implement CI/CD workflows (model build/test/deploy), monitor model performance in production, troubleshoot inference/pipeline issues.