• Design, fine-tune, and optimize advanced machine learning models for Arabic voice applications. • Work across the full development lifecycle, from data pipeline construction and model experimentation to inference optimization and production deployment. • 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.

• Design ranking and retrieval models for main page, related content, and search • Build real-time recommendation systems for videos, shorts and infinite scroll timelines • Develop end-to-end recommendation pipelines, contributing to backend • Explore algorithms for Shorts generation

• Experience daily immersion in automotive industry data • Collaborate with data science and engineering teams to build and maintain model training and forecasting/prediction ETL pipelines with appropriate data mappings, anomaly detection, feature engineering, and data imputation strategies • Deploy models and ETL pipelines in a reusable framework to generate predictions or forecasts for use in existing and net-new products • Lead the creation of model and data drift monitoring pipelines and reporting • Communications of technical topics to both lay and technical business stakeholders, product, and senior data science leadership while requiring minimal feedback for communication documents and presentations • Leads quarterly planning for major ML deployment projects with minimal supervision

• Leading ML research, experimentation, and development from concept to deployment • Designing and implementing novel ML/AI approaches, staying current with cutting-edge research and methodologies • Architecting ML pipelines, training infrastructure, and model evaluation frameworks • Collaborating with cross-functional teams: Working closely with product managers, and other stakeholders to ensure the AI/ML solutions align with the organization's goals and objectives • Communicating effectively: Clearly document your work and effectively communicate your findings, insights, and recommendations to both technical and non-technical stakeholders, fostering a collaborative and informed environment • Guiding ML engineering decisions including model selection, feature engineering, and optimization strategies • Translating academic insights into practical applications • Mentoring junior ML engineers and establish ML best practices across the team • Working closely with AI Systems Tech Leads to ensure delivery of optimized AI systems and services to production • Enforcing data governance and privacy: Follow processes and implement methods that ensure data governance, privacy, and security