ML Ops Engineer - Quantum ML Pipelines & Benchmarking

Silicon Quantum Computing; Sydney; On-site; AUD 140,000 - 195,000; Full time

Job summary

Silicon Quantum Computing (SQC) is hiring an ML Ops Engineer into Quantum Machine Learning to run a production reservoir on the Watermelon chip, handling data paths, training pipelines and model evaluation for multiple customer engagements at our Sydney facility.

You will implement reproducible experiments, track datasets and hyperparameters, and build benchmarks that stand up to scrutiny from customer data scientists while enabling seamless production deployment across the platform and services.

Qualifications

• 4+ years building and operating machine learning pipelines or data platforms in production.
• Strong Python, including the scientific and ML stack (NumPy, pandas, PyTorch or JAX, scikit-learn).
• Data engineering across batch and streaming workloads: pipeline orchestration (Airflow, Dagster, Prefect or equivalent), schemas, data validation and versioning.
• Practical MLOps: experiment tracking, model registries, reproducible training, CI/CD for models, and monitoring after deployment.
• Sound 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.
• Containers, and running workloads on infrastructure shared with other teams
• Git workflow, code review and testing, applied to research code as well as production code
• Working with data that carries commercial or regulatory constraints
• Working directly with commercial customers on their data and their problems
• Clear technical writing, and the ability to explain a result and its caveats to people who are not ML specialists

Responsibilities

• Own the data path from a customer dataset landing to features ready for training: ingestion, validation, cleaning, splitting, versioning and access control
• Build and run the training and evaluation pipelines that take quantum features from the Watermelon chip through to a trained, measured model
• Make experiments reproducible: track datasets, hyperparameters, code versions, chip identity and calibration state alongside every result
• Build the benchmarking harness that compares quantum features against classical deep learning baselines on equal terms, and keep it valid as models and datasets change
• Automate the path onto hardware: batching jobs against device queues, handling device unavailability and re-runs, and reconciling feature sets generated across different chip states
• Deploy models into customer-facing serving, and monitor them for accuracy, latency and data drift
• Build the pipeline scaffolding that lets a new customer engagement start from a working template instead of a blank notebook
• Instrument the pipelines so the team can see cost, throughput, device utilisation and where a run spent its time
• Work with Integration & Access on the QML APIs and SDKs, feeding back what the pipelines need from the platform
• Handle customer data under its commercial and privacy obligations, including segregation, retention and audit
• Balance model explainability, complexity and function when reporting results, considering more than model performance alone
• Document pipelines, data lineage and operational procedures so someone who did not run a result can reproduce it

Skills

Python

ML pipelines

NumPy

pandas

PyTorch

JAX

Airflow

Dagster

Prefect

CI/CD for models

Git

Data engineering

Tools

Airflow

Dagster

Prefect

Docker

Kubernetes

MLflow

Weights & Biases

Job description

Silicon Quantum Computing (SQC) is hiring an ML Ops Engineer into Quantum Machine Learning to run a production reservoir on the Watermelon chip, handling data paths, training pipelines and model evaluation for multiple customer engagements at our Sydney facility.

You will implement reproducible experiments, track datasets and hyperparameters, and build benchmarks that stand up to scrutiny from customer data scientists while enabling seamless production deployment across the platform and services.