{"id":1830371,"url":"https://alion.io/job/silicon-quantum-computing-ml-ops-engineer-quantum-machine-learning","title":"ML Ops Engineer, Quantum Machine Learning","company":{"id":50887,"name":"Silicon Quantum Computing","domain":"sqc.com","url":"https://alion.io/company/sqc","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sydney, Australia"],"countries":["AU"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":85000,"max_usd":204000,"period":"year","method":"role_country_seniority_unknown","sample_n":9},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Git","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Cirq","optional":true},{"name":"Dask","optional":true},{"name":"DuckDB","optional":true},{"name":"Feature Store","optional":true},{"name":"HPC","optional":true},{"name":"MLFlow","optional":true},{"name":"PennyLane","optional":true},{"name":"Qiskit","optional":true},{"name":"Ray","optional":true},{"name":"Ray Serve","optional":true},{"name":"Rust","optional":true},{"name":"SLURM","optional":true},{"name":"Spark","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"TorchServe","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-08-20T05:05:24Z","employer_posted_date":"2026-09-20","last_verified_at":"2026-10-06T00:02:13Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"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.\nBacked 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.\nOur 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.\nThis is SQC: building the future of computing while delivering quantum impact today.\nAbout the role\nWe are hiring an ML Ops Engineer into Quantum Machine Learning, the team that runs a production QML reservoir on the Watermelon chip: quantum feature generation that improves deep learning on customer problems, plus the models and pipelines around it. The scientists tune the physics, the devices and the models. You will build what the science depends on: the data path in, the training and evaluation pipelines, and the metrics that feed the roadmap.\nThe feature extractor is a quantum device with a queue and a calibrated state, so every training run has to record which chip and which calibration produced its features. Benchmarking devices and models against each other matters to our R&D work and to the results improving our device design.\nCustomer engagements drive the work. A dataset arrives, often needing data engineering and usually under NDA, and the team has to get from raw tables to a trained model with a defensible comparison against whatever the customer uses today. Doing that once is a research project. This role is doing it repeatedly, for several customers at a time, without the pipelines rotting between engagements.\nBased at our Sydney facility, you will work across the science, the platform and the customer-facing teams. This is a role for someone who can move research code into production without slowing the research down, and who wants to build benchmarks that stand up to scrutiny from a customer's own data scientists.\nRole responsibilities\nOwn the data path from a customer dataset landing to features ready for training: ingestion, validation, cleaning, splitting, versioning and access control\nBuild and run the training and evaluation pipelines that take quantum features from the Watermelon chip through to a trained, measured model\nMake experiments reproducible: track datasets, hyperparameters, code versions, chip identity and calibration state alongside every result\nBuild the benchmarking harness that compares quantum features against classical deep learning baselines on equal terms, and keep it valid as models and datasets change\nAutomate the path onto hardware: batching jobs against device queues, handling device unavailability and re-runs, and reconciling feature sets generated across different chip states\nDeploy models into customer-facing serving, and monitor them for accuracy, latency and data drift\nBuild the pipeline scaffolding that lets a new customer engagement start from a working template instead of a blank notebook\nInstrument the pipelines so the team can see cost, throughput, device utilisation and where a run spent its time\nWork with Integration & Access on the QML APIs and SDKs, feeding back what the pipelines need from the platform\nHandle customer data under its commercial and privacy obligations, including segregation, retention and audit\nBalance model explainability, complexity and function when reporting results, considering more than model performance alone\nDocument pipelines, data lineage and operational procedures so someone who did not run a result can reproduce it\nYour experience\nEssential\n4+ years building and operating machine learning pipelines or data platforms in production\nStrong Python, including the scientific and ML stack (NumPy, pandas, PyTorch or JAX, scikit-learn)\nData engineering across batch and streaming workloads: pipeline orchestration (Airflow, Dagster, Prefect or equivalent), schemas, data validation and versioning\nPractical MLOps: experiment tracking, model registries, reproducible training, CI/CD for models, and monitoring after deployment\nSound grasp of ML methodology, particularly evaluation: train/test hygiene, cross-validation, leakage, and how to build a benchmark that stands up to scrutiny from a customer's own data scientists\nContainers, and running workloads on infrastructure shared with other teams\nGit workflow, code review and testing, applied to research code as well as production code\nWorking with data that carries commercial or regulatory constraints\nWorking directly with commercial customers on their data and their problems\nClear technical writing, and the ability to explain a result and its caveats to people who are not ML specialists\nNice to have\nMLflow, or a comparable experiment tracking and model registry stack (Weights & Biases, Neptune)\nFeature stores, or any system where derived features need provenance back to their inputs\nTime series or signal data, which is where much of our customer work sits\nReservoir computing, kernel methods, or other approaches that treat a fixed nonlinear map as a feature generator\nQuantum computing exposure, including any of Qiskit, PennyLane, Cirq or similar (no physics degree required)\nHardware-in-the-loop pipelines, or scheduling work against instruments and devices with queues and downtime\nHPC and batch schedulers such as Slurm, and GPU cluster work\nModel serving at low latency (Triton, Ray Serve, TorchServe or equivalent)\nData engineering at scale (Spark, Dask, DuckDB, Parquet and columnar formats)\nCost and performance tuning across compute, storage and accelerator time\nRust or Go alongside Python\nEqual opportunity\nSQC 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.\nExport controls\nThis 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.\nAbout SQC\nSQC 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.\nAs 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.\nSQC is a high-accountability environment built on a simple principle: Every Atom Counts. 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