Senior Applied ML Engineer (Speech & Audio)

Job Description We are seeking a highly skilled Senior Applied Machine Learning Engineer with deep expertise in speech and audio technologies. In this role, you will design, fine-tune, and optimize advanced machine learning models for Arabic voice applications. You will work across the full development lifecycle, from data pipeline construction and model experimentation to inference optimization and production deployment. This position is ideal for engineers who are passionate about transforming cutting-edge research into scalable, low-latency systems that support natural and accurate Arabic speech interactions. Key Responsibilities Benchmark and evaluate TTS and ASR models using Arabic-specific test sets, measuring metrics such as Word Error Rate (WER), naturalness, and dialect coverage. Fine-tune generative models for voice cloning, zero-shot speaker adaptation, and speech synthesis. Build and maintain Arabic-focused data pipelines, including: Audio collection and preprocessing Diacritization (Tashkil) Data cleaning and augmentation Optimize model inference for production environments using: Quantization KV-cache tuning Streaming inference techniques Integrate and evaluate complete speech-to-speech conversational pipelines. Conduct experiments based on recent research papers and convert findings into production-ready solutions. Collaborate with engineering and product teams to deploy robust and scalable speech systems. Required Qualifications 5+ years of experience in Machine Learning, Applied AI, or AI Research. Strong programming skills in Python. Extensive hands-on experience with PyTorch and the Hugging Face ecosystem. Proven experience training and fine-tuning neural models for: Text-to-Speech (TTS) Automatic Speech Recognition (ASR) Audio codecs Deep understanding of modern speech architectures such as: Whisper Conformer HiFi-GAN Diffusion-based models Experience with audio processing techniques including: Voice Activity Detection (VAD) Speaker Diarization N