Deep Learning Quantitative Researcher

Millennium; Dubai; On-site; AED 450,000 - 850,000; Full time

Job summary

Millennium in Dubai is seeking a senior research-focused ML engineer to design and deploy core deep learning pipelines for quantitative research—from data prep and distributed training to production deployment.

The ideal candidate has top-tier academia with a PhD-level background, hands-on experience with large models, solid ML theory, and a track record in rigorous evaluation and reproducible research practices.

Qualifications

• Top-tier academic background with PhD-level training in a quantitative field.

Responsibilities

• Design and build core deep learning pipelines for applied quantitative alpha research—from data preparation to production deployment.
• Drive the research agenda using applied deep learning, owning the full empirical loop: problem formulation, model design, training, validation, and attribution.
• Uphold rigorous research discipline with out‑of‑sample hygiene, leakage prevention, and fair benchmarking against baselines.
• Advise on architecture selection and training diagnostics, review model designs, set standards for evaluation and promotion.
• Facilitate seamless model fitting and computation across teams via standardized training and inference interfaces.

Skills

Deep learning

Python

Distributed training

Model evaluation

Statistics & ML theory

Experiment tracking

Communication

Education

PhD level training in CS/Engineering/Physics/Math/Stats

Graduate from top-20 university

Tools

CUDA

PyTorch/TensorFlow

LLM tooling

Distributed training frameworks

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.

Requirements: