Jobs at Oxford Quantum Circuits

Build and operate secure batch and streaming data pipelines across cloud, private-cloud, on-premise, and datacentre environments. Develop governed datasets for machine learning and quantum workloads, ensuring data quality, lineage, access control, retention, and traceability. Create reliable evaluation and synthetic-data workflows, optimize pipeline performance and costs, and implement automated testing and CI/CD while collaborating with software, platform, security, ML, and infrastructure teams.

Build production-quality quantum software by translating algorithms and research prototypes into tested, maintainable components. Integrate quantum workloads with APIs, containerized services, Kubeflow workflows, data infrastructure, simulators, and QPUs. Diagnose issues across quantum and classical execution layers, design hybrid workflows, and apply CI/CD, testing, documentation, version control, and code review while collaborating across research, platform, infrastructure, and applications teams.

Develop reusable classical and scientific machine-learning algorithms for quantum and hybrid products. Responsibilities include implementing optimization, sampling, surrogate, generative, and multi-fidelity methods; building benchmark implementations and evaluator libraries; comparing classical and quantum approaches; and identifying workloads suited to quantum intervention. The role supports financial-services applications initially and requires cross-disciplinary collaboration, practical evaluation, and clear communication.

Develop and evaluate classical algorithms and machine-learning methods for banking, fintech, payments, fraud detection, credit risk, and transaction modeling. Build production-relevant baselines and synthetic-data benchmarks, collaborate with quantum algorithm engineers, integrate methods into applications, and assess privacy, calibration, robustness, operational usefulness, and model-risk trade-offs. Communicate results and limitations to technical and non-technical stakeholders.

Develop low-qubit quantum generative models for synthetic and rare-event data, including banking applications. Design, test, and benchmark QCBMs, qGANs, quantum Boltzmann methods, variational circuits, and sampling techniques against classical alternatives. Optimize implementations for current quantum hardware, validate them on simulators and QPUs, analyze experimental results, and package successful algorithms for OQC’s hybrid computing platform.

Develop and evaluate quantum algorithms for detection, prediction, and estimation workloads. Design measurement strategies, controlled experiments, and ablation studies to assess circuit costs, sample complexity, noise sensitivity, hardware portability, and quantum contribution. Benchmark theoretical expectations against real quantum hardware and help translate promising methods into repeatable capabilities for hybrid computing products. Collaborate across quantum research, software, product, and engineering teams.

Own the roadmap for OQC’s Quantum Model Platform and outcome products. Translate customer needs into focused requirements, measurable acceptance metrics, product claims, and commercial delivery plans. Align product, engineering, commercial, and partnership priorities while protecting platform coherence and reusability. Engage credibly with APIs, model development, heterogeneous compute, and deployment without owning system architecture.