Quantum ML Platform Engineer

Silicon Quantum Computing Pty Ltd; Sydney; On-site; AUD 150,000 - 190,000; Full time

Job summary

Silicon Quantum Computing Pty Ltd (SQC) is hiring an ML Ops Engineer for the Quantum Machine Learning team. You will manage data paths, training/evaluation pipelines, and reproducibility across multiple customer engagements on the Watermelon chip.

The role focuses on production-ready data pipelines, benchmarking, and integration with platform APIs. Based at our Sydney facility, you will move research code into production without slowing research, building benchmarks that withstand scrutiny from

Qualifications

• 4+ years building and operating ML pipelines in production.
• Proficient Python, with NumPy, pandas, PyTorch/JAX, scikit-learn.
• Data engineering across batch and streaming with orchestration (Airflow, Dagster, Prefect).
• Practical MLOps: experiment tracking, model registries, reproducible training, CI/CD for models, monitoring.
• Solid ML evaluation skills: train/test hygiene, cross-validation, leakage, benchmarking.
• Experience with containerized workloads and shared infrastructure.
• Git workflows, code reviews, and testing in research and production environments.
• Experience handling data with commercial or regulatory constraints.

Responsibilities

• Own the data path from customer dataset landing to features for training.
• Build and run training and evaluation pipelines from features to model.
• Make experiments reproducible: track datasets, hyperparameters, code versions, chip identity, calibration state.
• Build benchmarking harness comparing quantum features against classical baselines.
• Automate the path onto hardware: batching jobs, device queues, re-runs, and cross-chip state reconciliation.
• Deploy models into customer-facing serving and monitor accuracy, latency and data drift.
• Create scaffolding to start new customer engagements from templates.
• Instrument pipelines to show cost, throughput, device utilisation and run time.
• Collaborate with Integration & Access on QML APIs/SDKs and feed back pipeline needs.
• Handle customer data under commercial and privacy obligations, including segregation and retention.
• Balance explainability, complexity and function when reporting results.
• Document pipelines, data lineage and operational procedures for reproducibility.

Skills

Python

ML pipelines

Data engineering

MLOps

ML evaluation

Containers

Git & testing

Data governance

Customer-facing

Technical writing

Tools

MLflow

Weights & Biases

Neptune

Job description

Silicon Quantum Computing Pty Ltd (SQC) is hiring an ML Ops Engineer for the Quantum Machine Learning team. You will manage data paths, training/evaluation pipelines, and reproducibility across multiple customer engagements on the Watermelon chip.

The role focuses on production-ready data pipelines, benchmarking, and integration with platform APIs. Based at our Sydney facility, you will move research code into production without slowing research, building benchmarks that withstand scrutiny from