Deep Learning Quantitative Researcher

Millennium; Hong Kong; On-site; HKD 600,000 - 1,200,000; Full time

Job summary

Millennium in Hong Kong seeks a senior deep learning researcher to design and build the firm’s core DL pipelines for applied quantitative alpha research, from data preparation to production deployment.

You will own substantial portions of the research agenda, ensure rigorous out-of-sample validation, and collaborate across research and engineering to scale models and improve performance.

Qualifications

• 3–5 years of professional experience applying deep learning to large-scale problems, preferably in quantitative finance.
• Proof of end-to-end ownership of deep learning model lifecycle on production or research lines.
• Deep expertise in Python and a modern DL framework.
• Hands-on experience with large-scale model training: distributed GPUs, mixed precision, and profiling.
• Strong foundations in statistics, optimization, and ML theory.

Responsibilities

• Design and build deep learning pipelines for applied quantitative alpha research including data prep, distributed training, evaluation, and deployment.
• Drive a major portion of the research agenda with end-to-end empirical work: problem formulation, model design, training, validation, and attribution.
• Maintain rigorous research discipline with out-of-sample hygiene and honest benchmarking against baselines.
• Advise on architecture choices and training diagnostics; review model designs and promote evaluation standards.
• Enable smooth model fitting and computation across teams via standardized training interfaces and reusable components.

Skills

Deep learning

Python

DL framework

Distributed training

Mixed precision

Experiment tracking

Hyperparameter optimization

Statistics

Optimization

Communication

C++

CUDA

Education

PhD in Computer Science / Engineering / Physics / Mathematics / Statistics

Tools

C++

CUDA

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.