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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.…
An on-site Sydney infrastructure engineer who owns the physical and network layer of SQC's platform: racking, cabling and provisioning on-premise bare-metal compute clusters (including per-quantum-computer clusters), designing L2/L3 network fabrics, and driving it all as code with Ansible, Kubernetes, and core services like DNS/DHCP/NTP/PXE.
Build and operate ML pipelines for SQC's quantum-enhanced AI system, Watermelon. You will manage data ingestion, training/evaluation workflows, and experiment tracking while benchmarking quantum features against classical baselines using Python, PyTorch/JAX, and orchestration tools like Airflow or Dagster.
Senior role owning the end-to-end monitoring and telemetry stack for quantum computing infrastructure. Responsibilities include instrumenting Kubernetes, Ceph, and HPC environments, managing high-cardinality data, and designing low-noise alerting using Prometheus, Grafana, and OpenTelemetry.
Develop software to automate and orchestrate quantum qubit calibration protocols. You will build the orchestration layer, realtime services, and data stores that keep hardware within operational specs, working closely with physicists and using Python, Rust/C++, and scientific computing libraries.
Software Engineer building a quantum compiler pipeline to translate OpenQASM into hardware-specific instructions. You will own stages like logical compilation, optimization, and place-and-route using Rust, C++, and Python, working closely with quantum scientists to implement error correction and noise models.
Develops the realtime quantum control plane, including schedulers and compilers, to manage logical qubit operations and error correction. Works with Rust, C++, and Python on FPGA-based hardware with microsecond-level latency constraints.
Build and deploy machine learning models to automate the calibration of silicon qubits. You will develop systems for drift detection, uncertainty quantification, and sequential optimization using Python, PyTorch/JAX, and Bayesian methods to drive physical hardware operations.
Technical Project Manager at Silicon Quantum Computing in Sydney, owning end-to-end delivery schedules, budgets, risk management and the government/customer interface for multi-year US government-sponsored quantum computing programmes. Day to day spans hardware fabrication, software workstreams, subcontractors and investor-facing budget tracking.
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