Data Scientist - Voice AI Models

About the role

Clusterlab is looking for a Data Scientist with strong machine learning fundamentals and a deep interest in voice technology. The role builds and fine-tunes voice-related models from scratch, such as speech-to-text, speaker diarization, audio classification and LLM-integrated speech systems, covering the full stack from data collection to production deployment.

Key responsibilities

• Lead development of custom models for speech recognition, transcription, audio segmentation and speaker identification.
• Build data pipelines that collect, preprocess, clean and label large audio datasets.
• Fine-tune and evaluate open-source models (e.g. Whisper, wav2vec, HuBERT, Conformer) on proprietary datasets.
• Design experiments and benchmark models for quality, latency and domain adaptability.
• Work with product teams to embed voice capabilities into real-time applications such as live summarization, AI agents and call insights.
• Maintain scalable training, evaluation and inference workflows with modern ML tooling.
• Contribute to internal knowledge sharing and best practices around audio ML.

Required profile

• Strong experience with speech or audio ML (speech recognition, speaker diarization, voice activity detection).
• Hands-on experience building models from scratch and fine-tuning large models.
• Deep understanding of signal processing, feature extraction and data augmentation for audio.
• End-to-end ML pipeline experience and comfort with cloud platforms (GCP, AWS) and containerized environments.
• High agency and comfort in fast-paced, ambiguous environments.

Required skills

• Python and ML libraries: PyTorch, NumPy, Scikit-learn, Hugging Face.
• Nice to have: LLM + speech integration, real-time/streaming inference, multilingual ASR and Arabic dialects, DVC, MLflow or W&B.

What we offer

• The chance to build domain-defining voice technology from scratch.
• Exposure to real-world deployments and rapid iteration cycles.
• Mentorship and collaboration with a team of engineers and researchers.
• Flexible, remote-friendly work culture focused on ownership and outcomes.