Applied Machine Learning Specialist

Job Description The Applied Machine Learning Specialist will be responsible for researching, designing, and deploying advanced machine learning and deep learning models that address real-world challenges and drive innovation across products and services. The role focuses on optimizing neural network architectures, building intelligent systems, and developing scalable solutions in areas such as Natural Language Processing (NLP), neural network efficiency, and model automation. The specialist will collaborate closely with cross-functional teams, including researchers and engineers, to transform cutting-edge AI research into production-ready applications. Duties & Responsibilities: Research and Model Optimization - Conduct research in emerging areas of efficient neural network design, including quantization, pruning, compression, and neural architecture search. - Explore and implement novel differentiable compute primitives and optimization strategies. - Benchmark deep neural networks (DNNs) and improve their performance through systematic testing and refinement. AI System Design and Development - Design and build self-running artificial intelligence systems to automate predictive model workflows. - Develop AI algorithms with the capability to learn and generate accurate predictions based on complex data sets. - Run controlled tests and experiments, conduct statistical analyses, and interpret results to validate models. NLP and Algorithm Innovation - Develop innovative NLP algorithms and AI solutions to address both immediate use cases and long-term strategic needs. - Build quick Proof of Concepts (POCs) and own the development process through to scalable, production-ready implementations. - Apply advanced machine learning techniques in areas such as sentiment analysis, entity recognition, and language modeling. Data Analysis and Processing - Assess, clean, and organize large-scale datasets for training, validation, and deployment of machine learning models. - Work closely with data engineers to ensure the quality, relevance, and readiness of data for AI applications. Collaboration and Integration - Collaborate cross-functionally with research scientists and software engineers to integrate novel ML algorithms into product pipelines. - Translate research advancements into deployable tools and services that contribute to organizational objectives. - Provide technical insights and support across teams to ensure smooth integration of AI solutions. Documentation and Reporting - Document experiments, model parameters, methodologies, and results to ensure reproducibility and transparency. - Maintain clear records of development processes, performance benchmarks, and research findings. Education, Experience & Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. - 4-6 years of experience in applied machine learning, deep learning, or AI model development in a research or industry setting. - Strong background in deep learning, neural networks, and applied machine learning. - Proven ability to conduct AI research and translate findings into practical, real-world applications. - Proficient in programming languages such as Python, with experience in frameworks like TensorFlow, PyTorch, Hugging Face, or similar. - In-depth knowledge of machine learning concepts, model optimization techniques, and statistical analysis. - Familiarity with data processing, pipeline development, and cloud-based ML deployment is an advantage. - Experience with GPU-based model training and performance benchmarking. - Strong problem-solving and analytical thinking. - Excellent communication skills and the ability to collaborate in a cross-functional, research-driven environment. - Detail-oriented with a commitment to documenting work and adhering to best practices in AI development. - A continuous learner with curiosity about emerging AI trends and technologies. - Proficient in both Arabic and English languages. Technical Competencies - Neural Network Optimization (Pruning, Quantization, NAS) - AI Algorithm Design and Automation - Deep Learning and DNN Benchmarking - Natural Language Processing (NLP) - Data Analysis and Processing - Statistical Modeling and Experimentation - AI Research and Proof of Concept Development - Model Deployment and Integration