ML Engineer for Quantum Calibration & Control

Silicon Quantum Computing; Sydney; On-site; AUD 120,000 - 170,000; Full time

Job summary

Silicon Quantum Computing in Sydney is seeking a Machine Learning Engineer for Automated Calibration Services. You will build models that decide when calibration routines run, driving actions from schedules or events, with uncertainty quantified and validated on real qubit data.

The role collaborates with physicists and engineers to optimize device time, replace exhaustive sweeps with sequential methods, and ensure measurable impact of model-driven calibrations on qubit performance.

Qualifications

• 4+ years applying machine learning in production, ideally where the output drives a physical system.
• Strong 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 estimation and optimisation
• Git workflow, code review and testing applied to research code
• Clear technical writing

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

Skills

Python

NumPy

SciPy

pandas

scikit-learn

PyTorch

JAX

Bayesian optimization

Time series

Uncertainty quantification

Git

Technical writing

Tools

QCoDeS

Labber

pyvisa

SCPI

Job description

Silicon Quantum Computing in Sydney is seeking a Machine Learning Engineer for Automated Calibration Services. You will build models that decide when calibration routines run, driving actions from schedules or events, with uncertainty quantified and validated on real qubit data.

The role collaborates with physicists and engineers to optimize device time, replace exhaustive sweeps with sequential methods, and ensure measurable impact of model-driven calibrations on qubit performance.