Deep Learning Quantitative Researcher

About the Role

We seek an advanced deep learning quantitative researcher to join our team in Dubai. The ideal candidate possesses a strong academic foundation from a top-20 global university, ideally holding a PhD in Computer Science, Engineering, Physics, Mathematics, or Statistics. Preference is given to candidates with a gold medal from national or international olympiads and practical experience at leading quantitative trading firms or AI/technology companies.

Key Responsibilities

• Develop and maintain the company’s core deep learning pipelines that support quantitative alpha research, encompassing data handling, distributed training, evaluation, and deployment.
• Take ownership of significant research projects by applying deep learning methods through the entire empirical process including problem definition, model development, training, validation, and performance analysis.
• Maintain strict research discipline in a challenging domain by ensuring out-of-sample validity, preventing data leakage, and benchmarking against simpler models.
• Serve as the firm’s primary deep learning authority, advising on architecture choices, training diagnostics, model reviews, and setting evaluation standards.
• Enable smooth integration of model fitting and computation across teams through reusable components and standardized interfaces for training and inference.

Qualifications & Experience

• 3 to 5 years of professional experience applying deep learning at scale, ideally within quantitative finance; equivalent strong academic research experience with significant deep learning projects at leading AI or tech companies will also be considered.
• Demonstrated end-to-end responsibility over deep learning model lifecycles either through production systems or published research.
• Proficiency in Python and at least one modern deep learning framework.
• Experience with large-scale model training, including multi-GPU distributed training, mixed precision techniques, and performance optimization.
• Strong theoretical background in statistics, optimization, and machine learning.

Technical Expertise

• Expertise in contemporary deep learning architectures with sound judgment about model complexity.
• Techniques to handle low signal-to-noise ratio data such as regularization, ensembling, and rigorous validation protocols.
• Fluency in managing large-scale datasets including efficient storage formats, streaming data loaders, and correct point-in-time dataset construction.
• Experience with experiment management tools like tracking systems, hyperparameter optimization, and reproducible research workflows.
• Additional skills in C++ or CUDA optimization and familiarity with large language model tooling are advantageous.

Soft Skills

• Strong research discipline with the ability to design clear experiments and decisively terminate unproductive directions.
• Proactive collaboration by fostering partnerships across research and engineering teams.
• High ethical standards in managing sensitive models and data.
• A growth-oriented mindset to keep pace with evolving technology and methodologies.
• Excellent communication skills to clearly articulate model functioning and uncertainties to diverse audiences.