Senior Software Engineer – Calibration Operations

Oxford Quantum Circuits (OQC)

. Design and build software systems that automate calibration, benchmarking and bring-up of OQC’s quantum computers .

Posted 10/9/2026full-timeBarcelona • SpainSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong Python programming skills and experience in designing and delivering complex software systems, with a focus on distributed systems and event-driven architectures. Proficient in developing observable systems and improving reliability in scientific workflows.

Highest-signal resume keywords

Python ProgrammingDistributed SystemsEvent-Driven ArchitecturesObservability DesignCI/CD Practices

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Hard Skills

Software ArchitectureSystem DesignAPI DesignTesting and DebuggingCalibrationBenchmarkingEvent StreamingMessage BrokersData ModelsIntegration Contracts

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

ContainersCloud-Native DeploymentContinuous IntegrationContinuous DeliveryMetricsDistributed TracingDashboardsAlerting

Certifications & Qualifications

Postgraduate Qualification

Industry Keywords

Quantum ComputingCalibration ServicesAutomated ExperimentationMachine Learning InfrastructureScientific Workflows

Tech Stack

Tools & technologies

CloudDistributed SystemsPython

About the role

Key responsibilities & impact

Design and build software systems that automate calibration, benchmarking and bring-up of OQC’s quantum computers
Support automated system bring-up, daily calibration and performance remediation for production systems and research environments
Design architectures for scalable calibration and benchmarking services
Develop event-driven systems to orchestrate calibration workflows and communicate status and results
Define event schemas, service interfaces and data flows across distributed systems
Build observability through structured logging, metrics, distributed tracing, dashboards and alerting
Improve reliability, fault tolerance and recovery behaviour of long-running scientific workflows
Integrate calibration services with control, data, compiler and platform infrastructure
Diagnose and resolve issues across services, workflows, infrastructure boundaries and production environments
Develop tools that make systems easier to operate and support
Establish engineering patterns for testing, deployment, monitoring and operational readiness
Own software projects from requirements and architectural design through deployment and ongoing operation
Contribute to architectural decisions and share software-engineering knowledge across the team

Requirements

What you’ll need

Strong Python programming skills and experience applying modern software-engineering practices
Experience designing and delivering complex software systems from concept to production, with a strong understanding of system design, software architecture and service boundaries
Experience with distributed systems and asynchronous or event-driven architectures
Practical experience with event streaming, message brokers, queues or publish-subscribe systems
Experience designing observable systems using logs, metrics, distributed tracing, dashboards and alerts
Understanding of reliability concepts such as retries, idempotency, failure recovery, backpressure and graceful degradation
Experience designing APIs, data models, event schemas and integration contracts
Strong testing and debugging skills across local, development and production environments
Experience with containers, continuous integration and delivery (CI/CD), and cloud-native deployment environments
Ability to communicate architectural decisions clearly and collaborate across software, engineering and scientific disciplines
Degree-level knowledge in computer science, software engineering, physics or a related discipline
Quantum computing knowledge is helpful, but not required
Knowledge of quantum computing or an interest in learning about quantum systems is nice to have
Experience with calibration, benchmarking, optimisation or automated experimentation is nice to have
Familiarity with machine-learning infrastructure, reinforcement learning or black-box optimisation is nice to have
Experience integrating software with laboratory equipment, control systems or physical hardware is nice to have
Experience developing user-facing operational or scientific tools is nice to have
Contributions to open-source projects, technical publications or conference presentations are nice to have
A postgraduate qualification, such as an MSc or PhD, or equivalent experience is nice to have