Deep Learning Quantitative Researcher

About the Role

We are seeking a highly skilled Deep Learning Quantitative Researcher to join our team in Dubai. Candidates should possess exceptional academic credentials, preferably from a top 20 global university such as MIT, Harvard, Princeton, Stanford, or Caltech, with PhD-level expertise in Computer Science, Engineering, Physics, Mathematics, or Statistics. Preference will be given to those with notable accomplishments like gold medals from elite national or international olympiads (IMO, CMO, IOI, NOI, IPhO, CPhO). Experience in large-scale deep learning applications within quantitative trading firms or leading AI/technology companies is highly valued.

Key Responsibilities

• Develop and maintain the firm's central deep learning workflows tailored for applied quantitative alpha research, encompassing all stages from data preprocessing and distributed training to model evaluation and operational deployment.
• Lead substantial portions of the research agenda by applying advanced deep learning methodologies, managing the entire experimental process including problem definition, model architecture, training, validation, and performance analysis.
• Ensure rigorous research standards in environments characterized by low signal-to-noise ratios, employing strict out-of-sample validation, preventing data leakage, and benchmarking against simpler baseline models.
• Serve as the organization’s authoritative deep learning expert by providing guidance on model architectures, training diagnostics, reviewing designs, and establishing evaluation and deployment standards.
• Enable efficient interaction of model fitting and computation across teams through standardized interfaces and development of reusable components for training and inference.

Qualifications & Experience

• Between 3 and 5 years' experience deploying deep learning solutions to complex, large-scale problems, preferably within quantitative finance; equivalent strong PhD research with hands-on experience at top AI/tech companies is also acceptable.
• Demonstrated complete ownership over at least one significant deep learning production pipeline or research publication.
• Advanced proficiency in Python alongside a modern deep learning framework.
• Expertise in large-scale model training including distributed multi-GPU approaches, mixed precision techniques, and performance optimization.
• Strong grounding in statistics, optimization, and machine learning theoretical principles.

Technical Skills

• Mastery over contemporary deep learning architectures and discernment in preferring simpler models when suitable.
• Techniques for learning in low signal-to-noise environments, including regularization, ensembling, and robust validation practices.
• Experience managing extensive datasets with efficient columnar data formats, streaming loaders, and precise time-correct dataset construction.
• Familiarity with experiment management tools such as experiment tracking systems, hyperparameter tuning, and reproducible research frameworks.
• Bonus: Proficiency in C++ or CUDA for performance optimization and familiarity with large language model tooling as a research accelerator.

Personal Attributes

• Strong research sensibility and discipline, able to design clean experiments and decisively discard unsupported hypotheses.
• Collaborative approach, fostering robust partnerships between research and engineering teams.
• High ethical standards especially in the management of sensitive data and models.
• A growth-oriented mindset that keeps pace with rapid advancements in the field.
• Excellent communication skills for articulating model behavior and uncertainties to both technical and non-technical stakeholders.

Application Instructions

Interested applicants should submit their resumes via email referencing REQ-30088.