Machine Learning Engineer - Automated Calibration Services

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 whenReplace exhaustive parameter sweeps with sequential optimisation, using Bayesian optimisation, active learning or comparable methods, to reach the same tuning outcome in less device timeApply computer vision and signal analysis to device measurement data where those methods outperform simpler alternativesBuild the feature and telemetry pipelines out of the calibration store that these models depend onQuantify uncertainty, so the orchestration can separate a high-confidence recommendation from a low-confidence one and act accordinglyValidate models against held-out device data, including the case where the device has changed since trainingDeploy models into the calibration loop with monitoring, fallback paths and a kill switchDistinguish real drift and defects from measurement noise, and set the thresholds that trigger actionWork with physicists to encode what they already know as priors and constraints rather than making the model learn it twiceInstrument the loop so the effect of a model-driven calibration on qubit performance is measurable after the factFeed device characterisation and noise models back to Compiler Services and Control & Error CorrectionDocument models, assumptions and failure modes, so an automated decision can be explained to the owner of the affected device Your experience Essential 4+ years applying machine learning in production, ideally where the output drives a physical systemStrong Python and the scientific stack: NumPy, SciPy, pandas, scikit-learn, and PyTorch or JAXSequential decision-making under expensive experiments: Bayesian optimisation, Gaussian processes, active learning or banditsTime series modelling, and anomaly or drift detection on real telemetryUncertainty quantification, and knowing what a calibrated confidence interval is worthValidating models where data is scarce, correlated and non-stationary, and recognising an implausibly strong resultDeploying models into a loop with monitoring, alerting and a fallback pathWorking with scientists on a problem where the domain knowledge is theirs and the automation is yoursNumerical work: curve fitting, parameter estimation and optimisationGit workflow, code review and testing applied to research codeClear technical writing Nice to have Computer vision on instrument, microscopy or measurement data, with OpenCV or PyTorchReinforcement learning, or control policies learned rather than specifiedQubit calibration, tune-up or device characterisation, on any qubit modalityControl theory and closed-loop feedbackLaboratory instrument control: QCoDeS, Labber, pyvisa or SCPIMLflow or comparable experiment tracking, and reproducibility practiceInference under a latency budget, including on edge hardware or FPGAWorkflow orchestration such as Airflow, Dagster, Prefect or TemporalQuantum computing exposure of any kind (no physics degree required)Publications, open source contributions, or an experimental automation project you are willing to talk through Equal opportunity SQC is an equal opportunity employer. We value diverse perspectives and experiences, and encourage applications from candidates who may not meet every listed requirement. If you’re excited about the role and believe you can contribute, we encourage you to apply. Export controls This position may require access to export-controlled information or technology. Employment may be subject to applicable export control laws and may require eligibility assessment based on factors such as nationality, citizenship, or residency, and, where necessary, obtaining relevant export licenses or approvals. About SQC SQC was founded by renowned physicist and materials scientist Michelle Simmons, who pioneered the field of atomic electronics, including the development of the world’s first single-atom transistor and the first integrated circuit built with atomic precision. Our Chair, Simon Segars, former CEO of Arm, is a leader in the semiconductor industry and was instrumental in developing the processors that powered the mobile computing revolution. As a full-stack company with in-house QPU manufacturing, SQC can design, produce and test new quantum chips in under a week, enabling rapid iteration and a decisive advantage in the race to build the world’s first commercial-scale quantum computer. SQC is a high-accountability environment built on a simple principle: Every Atom Counts. If you’re looking to play a meaningful role in building the next frontier of computing, we’d love to hear from you.