26 Jun 2026

Machine Learning Engineer

IMC Trading

Sydney

26 Jun 2026

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IMC is seeking a Machine Learning Engineer to build systems for large-scale ML model training and deployment in financial services. The role involves developing distributed training pipelines, low-latency inference, and GPU acceleration. Requires 3+ years ML experience, Python, CUDA, C++, and knowledge of PyTorch, TensorFlow, or JAX.

Core AIOn-siteFull-timeEntry LevelPythonCUDA

Machine Learning Engineer

Apply

Core AIOn-siteFull-timeEntry LevelPython

Salary

Not specified

Work Location

Sydney, New South Wales, Australia, AU

Work Model

On-site

Experience Required

3 years

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

PythonCUDAC++PyTorchTensorFlowJAXGPU programmingCuDNNTensorRTDistributed trainingHorovodNCCL

Soft Skills

CollaborationInnovation

Preferred Qualifications

Technical (Nice-to-have)

Cloud platformsOrchestration toolsOpen source contribution

Key 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

Machine LearningFinancial ServicesEngineeringPythonCUDAPyTorchGPU accelerationDistributed systemsOn-siteFull-time

Key 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