Machine Learning Engineer, Automated Calibration Services

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

Job summary

Silicon Quantum Computing (SQC) in Sydney is seeking a Machine Learning Engineer for Automated Calibration Services. You will build models to predict qubit drift, optimise calibration routines, and validate decisions against real-device data.

Based at our Sydney facility, you’ll work with physicists and engineers to drive calibration with uncertainty-aware decisions, aiming to minimize device time while maintaining reliability.

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

Time series modelling

Uncertainty quantification

Bayesian optimisation

Gaussian processes

Active learning

Sequential decision making

Model deployment

Tools

PyTorch

JAX

NumPy

SciPy

Pandas

Job description

Machine Learning Engineer, Automated Calibration Services

Sydney

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

Your experience

• 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

Nice to have

• Computer vision on instrument, microscopy or measurement data, with OpenCV or PyTorch
• Reinforcement learning, or control policies learned rather than specified
• Qubit calibration, tune-up or device characterisation, on any qubit modality
• Control theory and closed-loop feedback
• Laboratory instrument control: QCoDeS, Labber, pyvisa or SCPI
• MLflow or comparable experiment tracking, and reproducibility practice
• Inference under a latency budget, including on edge hardware or FPGA
• Workflow orchestration such as Airflow, Dagster, Prefect or Temporal
• Quantum 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.

We collect information about your nationality, work rights and citizenships solely to meet legal obligations. It does not affect your candidacy.

SQC is willing and able to sponsor candidates in the event they do not hold working entitlements in the country the role is based.