Silicon Quantum Computing (SQC) is at the forefront of global efforts to build the world’s first commercial-scale quantum computer, while delivering quantum-enhanced AI and simulation products to customers today.

Backed by over 25 years of technological excellence, SQC is a full-stack quantum computing company that leverages its proprietary manufacturing process to engineer atomic qubits in silicon with 0.13 nanometer precision. It is the most precise semiconductor manufacturing in the world, enabling systems with world-leading algorithmic fidelity, and a decisive advantage in the global quantum computing race.

Our products are commercially deployed and generating revenue. Watermelon, our quantum-enhanced AI system, is delivering superior results on real-world problems across energy, telecom and finance. Quantum Twins, our simulation platform, provides unparalleled ability to model quantum systems, accelerating molecule and materials discovery.

This is SQC: building the future of computing while delivering quantum impact today.

About The Role We are hiring a Machine Learning Engineer into Automated Calibration Services, the team that keeps qubits inside spec while programs are running. Physicists design the calibration protocols; you will build the models that decide when they run, drive them from a schedule, a compiler request or a monitoring event, and establish afterwards whether they worked.

The open questions are inference problems: which qubit is about to drift out of spec, which routine recovers it fastest, whether an optimiser can replace a parameter sweep in a fraction of the device time, and whether a change in a measurement is a device defect or noise. Device time is scarce, so an unnecessary calibration costs program execution.





Model output triggers a physical operation on a device. Each decision needs an uncertainty estimate, a fallback when confidence is low, and instrumentation to establish afterwards whether the qubit improved. Data is expensive, the process is non-stationary, and a model trained on last month’s device may not hold.

Based at our Sydney facility, you will work daily with the physicists who own the domain knowledge and the engineers who run the calibration stack. This is a role for someone who wants their models to drive real hardware, and who treats uncertainty quantification and validation as the substance of the work rather than an afterthought.

Role Responsibilities

Build models that predict qubit drift and device health, and turn those predictions into decisions about which routine runs and when

Replace exhaustive parameter sweeps with sequential optimisation, using Bayesian optimisation, active learning or comparable methods, to reach the same tuning outcome in less device time

Apply computer vision and signal analysis to device measurement data where those methods outperform simpler alternatives

Build the feature and telemetry pipelines out of the calibration store that these models depend on

Quantify uncertainty, so the orchestration can separate a high-confidence recommendation from a low-confidence one and act accordingly





Validate models against held‑out device data, including the case where the device has changed since training

Deploy models into the calibration loop with monitoring, fallback paths and a kill switch

Distinguish real drift and defects from measurement noise, and set the thresholds that trigger action

Work with physicists to encode what they already know as priors and constraints rather than making the model learn it twice

Instrument the loop so the effect of a model‑driven calibration on qubit performance is measurable after the fact

Feed device characterisation and noise models back to Compiler Services and Control & Error Correction

Document models, assumptions and failure modes, so an automated decision can be explained to the owner of the affected device

Essential Your experience

4+ years applying machine learning in production, ideally where the output drives a physical system

Solid Python and the scientific stack: NumPy, SciPy, pandas, scikit-learn, and PyTorch or JAX

Sequential decision‑making under expensive experiments: Bayesian optimisation, Gaussian processes, active learning or bandits

Time series modelling, and anomaly or drift detection on real telemetry

Uncertainty quantification, and knowing what a calibrated confidence interval is worth

Validating models where data is scarce, correlated and non‑stationary, and recognising an implausibly strong result

Deploying models into a loop with monitoring, alerting and a fallback path

Working with scientists on a problem where the domain knowledge is theirs and the automation is yours

Numerical work: curve fitting, parameter est

📌 Machine Learning Engineer, Automated Calibration Services (Sydney)
🏢 Silicon Quantum Computing
📍 Sydney