Deep Learning Quantitative Researcher

Talentmate; Dubai; On-site; AED 300,000 - 540,000; Full time

Job summary

Talentmate in Dubai seeks a senior deep learning researcher to design and build the firm’s core DL pipelines for quantitative alpha research, spanning data prep, distributed training, evaluation, and production deployment.

You will own a substantial portion of the research agenda, from problem formulation to model design, training, validation, and performance attribution, while maintaining rigorous out-of-sample discipline and strong benchmarking.

Qualifications

• Top-tier academic background and PhD-level training in a quantitative field.
• 3–5 years applying deep learning to large-scale problems.
• Strong Python and modern DL framework expertise.
• Experience with large-scale model training and optimization.
• Solid foundations in statistics and ML theory.

Responsibilities

• Design and build the firm’s core deep learning pipelines for applied quantitative alpha research—from data preparation and distributed training through evaluation and production deployment.
• Drive a significant part of the research agenda using applied deep learning techniques, owning the full empirical loop: problem formulation, model design, training, validation, and performance attribution.
• Uphold rigorous research discipline in a low signal-to-noise domain—strict out-of-sample hygiene, leakage prevention, and honest benchmarking against simpler baselines.
• Act as the firm’s central point of deep learning expertise: advise on architecture selection and training diagnostics, review model designs, and set standards for how models are evaluated and promoted.
• Facilitate the seamless flow of model fitting and model computation across teams and systems through standardized training and inference interfaces and reusable components.

Skills

Deep learning

Python

Distributed training

Model evaluation

Research communication

Education

PhD-level training in relevant field

Tools

PyTorch

TensorFlow

CUDA

C++

LLM tooling

Job description

Job Description

Preferred Candidate Profile

• Top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech).
• PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics.

Preferred

• Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) strongly preferred.
• Practical, hands‑on experience with large-scale, end-to‑end deep learning at a top-tier quantitative.

Trading Firm Or a Leading AI/technology Company Preferred

Key Responsibilities

• Design and build the firm’s core deep learning pipelines for applied quantitative alpha research—from data preparation and distributed training through evaluation and production deployment.
• Drive a significant part of the research agenda using applied deep learning techniques, owning the full empirical loop: problem formulation, model design, training, validation, and performance attribution.
• Uphold rigorous research discipline in a low signal-to-noise domain—strict out-of-sample hygiene, leakage prevention, and honest benchmarking against simpler baselines.
• Act as the firm’s central point of deep learning expertise: advise on architecture selection and training diagnostics, review model designs, and set standards for how models are evaluated and promoted.
• Facilitate the seamless flow of model fitting and model computation across teams and systems through standardized training and inference interfaces and reusable components.

Qualifications & Experience

• 3–5 years of professional experience applying deep learning to large-scale problems, ideally in quantitative finance. A strong PhD research record plus hands‑on experience training large models at a leading AI/technology company will be considered in lieu of direct quant experience.
• Proven end-to‑end ownership of the deep learning model lifecycle on at least one significant production system or published research line.
• Deep expertise in Python and a modern DL framework.
• Hands‑on experience with large-scale model training: distributed/multi-GPU training, mixed precision, and throughput profiling and optimization.
• Strong foundations in statistics, optimization, and machine learning theory.

Hard Skills & Technical Knowledge

• Command of modern deep learning architectures, and the judgment to know when a simpler model should win.
• Practical technique for low signal-to-noise learning: regularization, ensembling, and validation protocols that survive out-of-sample.
• Experience with large‑scale datasets — efficient columnar formats, streaming data loaders, and point‑in‑time‑correct dataset construction.
• Fluency with experiment‑management tooling: experiment tracking, hyperparameter optimization, and reproducible research environments.
• Working knowledge of C++ or CUDA-level optimization a plus; familiarity with LLM tooling as a research accelerant a plus.

Soft Skills

• Research Taste & Rigor: Designs clean experiments and kills ideas quickly when the evidence says so.
• Proactive Collaboration: Builds strong partnerships across research and engineering.
• High Integrity: Upholds rigorous ethical standards in handling sensitive data and models.
• Growth Mindset: Stays current with a fast-moving field and adopts what works.
• Superb Communication: Explains model behavior and uncertainty to technical and nontechnical audiences.