Deep Learning Quantitative Researcher

Millennium; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Millennium seeks a senior deep learning scientist to 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.

You will own the full empirical loop, from problem formulation to training, validation, and performance attribution, while upholding strict out-of-sample discipline and collaborating across research and engineering teams.

Qualifications

• 3–5 years of deep learning experience on large-scale problems.
• End-to-end ownership of the DL lifecycle on production systems.
• Strong Python and modern DL frameworks.

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 lifecycle

Statistics

ML theory

C++/CUDA

Education

PhD in CS/Engineering/Physics/Math/Statistics

Tools

PyTorch

TensorFlow

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.