Machine Learning Engineer

ittihad medical centre; Hong Kong; On-site; HKD 500,000 - 700,000; Full time

Job summary

ittihad medical centre in Hong Kong is seeking a Machine Learning Engineer to develop and optimize large-scale ML models impacting trading strategies. You'll work closely with researchers and engineers, utilizing GPU acceleration and open-source tools to create efficient solutions.

This role requires expertise in Python, CUDA, and experience with real-time ML pipelines. Join us to shape the technology and trading capabilities of IMC!

Qualifications

• 3+ years of experience in machine learning with a focus on training or inference systems.
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments.
• Strong engineering skills, proficiency in Python, CUDA, or C++.

Responsibilities

• Develop large-scale distributed training pipelines to manage datasets and complex models.
• Build and optimize low-latency inference pipelines to ensure real-time predictions.
• Collaborate with quantitative researchers to automate ML experiments and hyperparameter tuning.

Skills

Machine Learning

Python

CUDA

C++

Distributed Training

Real-time ML Pipelines

Tools

PyTorch

TensorFlow

JAX

CuDNN

TensorRT

Job description

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of IMC’s technology and trading capabilities.

Your Core Responsibilities

• Develop large-scale distributed training pipelines to manage datasets and complex models
• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
• Develop libraries to improve the performance of machine learning frameworks
• Maximize performance in training and inference using GPU hardware and acceleration libraries
• Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
• Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities
• Dig into the internals of open-source ML tools to extend their capabilities and improve performance

Your Skills and Experience

• 3+ years of experience in machine learning with a focus on training or inference systems
• Hands-on experience with real-time, low-latency ML pipelines in high-performance environments is a strong plus
• Strong engineering skills, including Python, CUDA, or C++
• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
• Exposure to cloud platforms and orchestration tools
• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus