Applied Deep Learning Quant 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

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.