About This Role
We are seeking an accomplished Quantum AI Engineer to play a pivotal role in designing and deploying machine learning models that integrate with our cutting-edge quantum processors. Based out of UNSW Sydney, you will sit within the Software Engineering team and collaborate closely with quantum engineers, hardware specialists, and customer organisations to build high-impact ML pipelines on our Watermelon quantum devices.
This is an ideal opportunity for someone who thrives at the intersection of cutting-edge AI and quantum technology. You will help shape real-world applications across finance, energy, telco, defence, and beyond – working with internal and external ML experts to deliver tailored, outcome-driven solutions that push the boundaries of what's possible.
Key Responsibilities:
* Design, develop, and deploy machine learning models that integrate with our quantum systems.
* Collaborate with internal quantum engineering teams and external customers to define use-cases and deliver fit-for-purpose ML solutions.
* Translate business requirements into technical problem statements and model architectures.
* Build and optimise ML pipelines for classification, inference, and decisioning tasks.
* Advise customer ML teams on model selection, tuning, and integration with quantum-enhanced workflows.
* Work closely with quantum physicists and software engineers to realise result-driven AI/ML pipelines on multi-qubit quantum devices.
* Evaluate and recommend tools, frameworks, and techniques to improve model performance and scalability.
* Stay abreast of emerging trends in machine learning, quantum computing, and applied mathematics.
Your Experience:
* Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, or a related field.
* Proven track record in developing and deploying ML models with measurable business impact.
* Deep understanding of ML paradigms, architectures, and optimisation techniques.
* Strong mathematical foundation, with exposure to quantum computing or quantum mechanics (academic or practical).
* Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
* Experience with cloud platforms (AWS, Azure, or Google Cloud) and big data technologies.
* Familiarity with data engineering practices and pipeline development.
* Ability to define clear problem statements and select appropriate modelling approaches.
* Strong communication skills and ability to collaborate across technical and non-technical teams.