Machine Learning Researcher

AAA Global; Hong Kong; Sur place; HKD 700 000 - 1 200 000; Plein temps

Résumé du poste

AAA Global in Hong Kong seeks a high-impact researcher to design and own the firm's deep learning engine behind alpha research. You will build core pipelines, from data prep and distributed training to evaluation and production deployment, and act as a central source of DL expertise across the organization.

This role blends applied modeling with scientific rigor, requires a strong publication/competition track, and offers autonomy to shape research directions and collaborate with teams on

Qualifications

• Master's degree or above in computer science or related field.
• Proficient in PyTorch or TensorFlow with rapid model development.
• Solid C++ programming foundation.
• Awards in ACM/ICPC, NOIP/NOI or Kaggle performance encouraged.
• Publications in ML/DM or recommender systems preferred.

Responsabilités

• Design and build core DL pipelines for applied quantitative alpha research.
• Own a significant part of the research agenda end-to-end.
• Set standards for research discipline and honest benchmarking.
• Advise on architecture selection and training diagnostics.
• Build reusable training and inference components for cross-team use.

Connaissances

PyTorch

TensorFlow

C++

Deep learning

Formation

Master's degree in Computer Science

Description du poste

We are helping a top tier quantitative hedge fund looking for best talent to build and own the deep learning engine behind the firm's alpha research. You will design the core pipelines, drive a meaningful share of the research agenda end-to-end, and act as a central point of deep learning expertise across the organization. It is a high-impact, high-autonomy role for a researcher who pairs strong applied modeling with genuine scientific rigor.

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.
• Own a significant part of the research agenda, running the full empirical loop: problem formulation, model design, training, validation, and performance attribution.
• Set the standard for research discipline in a low signal-to-noise domain — strict out-of-signal hygiene, leakage prevention, and honest benchmarking against simpler baselines.
• Serve as a go-to source of deep learning expertise: advise on architecture selection and training diagnostics, review model designs, and shape how models are evaluated and promoted.
• Build standardized training and inference interfaces and reusable components that let models flow smoothly across teams and systems.

Requirements

• A master's degree or above in computer science or related fields from a prestigious university
• Excellent programming skills, proficient in Pytorch/Tensorflow, capable of rapidly developing and implementing various cutting-edge deep learning models based on one of the frameworks. Improving and tunning models with innovative ideas. A solid foundation in C++ programming is required.
• Priority will be given to candidates who have won awards in competitions such as ACM/ICPC, NOIP/NOI, and those with outstanding performance in machine learning competitions like Kaggle.
• Candidates with publications in international conferences (including but not limited to NIPS, 1. Great People. We’re curious Self-motivated candidates are preferred. KDD, CVPR, ICML, ACL, AAAI, COLT, AISTATS, WWW, SIGIR, IJCAI) or journals in the fields of machine learning, data mining, and recommendation systems are preferred.
• Passion for the quantitative industry and a clear career development direction is essential.