Machine Learning Engineer – Distributed AI & GPU Systems - HFT - Sydney
Core
Build high-performance machine learning infrastructure for large-scale training and real-time inference using distributed computing and GPU acceleration.
Role type
Senior IC machine learning engineer (distributed AI & GPU systems)
Builds
Large-scale distributed training pipelines, low-latency inference pipelines, and high-performance ML libraries
Domain
High-frequency trading (HFT), distributed computing, GPU acceleration
Deliverable
production ML models
Required skills
Python, CUDA, C++, PyTorch, TensorFlow, JAX, GPU programming, distributed training, low-latency system design
Preferred skills
Real-time ML systems, cloud platforms, orchestration tools, open-source contributions
Technologies
CuDNN, TensorRT, Horovod, NCCL
Responsibilities
Develop large-scale distributed training pipelines; Build and optimise low-latency inference pipelines; Develop high-performance libraries that enhance ML frameworks; Maximise training and inference performance through GPU hardware; Design scalable model frameworks; Automate ML experimentation and hyperparameter tuning
Seniority
Senior, hands-on IC
