About Qblox
Qblox is a deep-tech scaleup whose mission is to accelerate the worldwide race towards scalable quantum computers. We do this by providing some of the world's most advanced quantum control stacks to laboratories and quantum startups across the globe.
Our hardware and software sit right next to the quantum chips, giving experimentalists precise control and readout of their qubits. Through our open-source interfaces qblox-scheduler and qblox-instruments, users can write and execute experiments and algorithms using Python – from defining quantum circuits to compiling down to our quantum assembly, Q1ASM.
We offer a dynamic environment where engineering meets cutting-edge physics research.
About the role
You'll join a growing DevOps team that spans FPGA, embedded systems, and high-level software — and you'll be hands-on from day one. The team is responsible for CI/CD pipelines, infrastructure automation, and helping engineering teams across Qblox adopt modern DevOps practices. There's a large backlog to tackle and real scope to shape how things are done.
This is a technical role with a broad remit. You'll spend most of your time building and maintaining pipelines and infrastructure, but you'll also be the person who helps other teams work better — improving how software is built, tested, and delivered.
What you will do
● Build and maintain CI/CD pipelines for FPGA, embedded, and software projects
● Automate build, test, and deployment workflows to reduce manual effort and improve
reliability
● Improve infrastructure reliability, scalability, and monitoring
● Work with our current tooling: Podman, Ansible, Terraform, GCP, libvirt + QEMU (KVM)
● Define and apply secure DevOps practices and contribute to compliance frameworks
● Integrate governance and change control into fast-paced development workflows
● Partner with engineering teams to drive adoption of DevOps practices and continuous
improvement
● Contribute to backlog management and help define ways of working as the team scales
What we're looking for
● 3+ years of experience in a DevOps or infrastructure engineering role
● Strong background in CI/CD pipeline design, build automation, and deployment workflows
● Hands-on experience with containerisation and orchestration tooling (Podman, Docker, or similar)
● Experience with infrastructure-as-code tools such as Ansible or Terraform
● Cloud platform experience, preferably GCP
● Solid understanding of Linux environments and scripting
● Working knowledge of security, compliance, and governance practices in DevOps contexts
● Strong communication skills and a service-oriented mindset — you're as comfortable helping a team unblock as you are fixing infrastructure
Nice-to-haves
● Experience with virtualisation tooling such as libvirt, QEMU, or KVM
● Familiarity with FPGA toolchains and embedded development workflows
● Experience driving DevOps adoption or cultural change within engineering teams
● Background in a high-tech, R&D-driven startup or scaleup (semiconductor, hardware, scientific instrumentation)
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The integration of DevOps engineering within the quantum control layer represents a critical transition from laboratory-scale experimentation to industrial-grade systems reliability. As quantum hardware modalities mature, the structural necessity for automated infrastructure becomes paramount to managing the high-dimensional complexity of hybrid classical-quantum workflows. This role type serves as a primary stabilizer within the deep-tech value chain, ensuring that the software-hardware interface remains resilient against the rapid iteration cycles characteristic of emerging Technology Readiness Levels (TRLs). By institutionalizing continuous integration and deployment protocols for specialized embedded and FPGA toolchains, this function mitigates the systemic risks associated with manual configuration errors in high-precision environments. Market signals from the Quantum Economic Development Consortium and national technology roadmaps underscore that scalable quantum computing is fundamentally dependent on the maturation of this operational foundation to bridge the gap between scientific discovery and commercial utility.
The quantum ecosystem is currently navigating a decisive shift where the primary bottleneck to scalability has expanded from fundamental physics to include the architectural robustness of the control stack. As organizations move toward million-qubit systems, the complexity of orchestrating heterogeneous environments—spanning bare-metal FPGA resources, virtualized containers, and cloud-native backends—creates significant integration friction. DevOps practitioners in this sector are the essential architects of the "quantum-classical bridge," tasked with reconciling the deterministic requirements of enterprise software engineering with the non-deterministic nature of quantum information science. This structural alignment is necessary to support the increasing data throughput requirements of production-grade quantum processors.
Workforce scarcity is particularly acute for engineering roles that possess the cross-domain literacy required to automate workflows involving scientific instrumentation and low-level firmware. Current industry dynamics, influenced by the convergence of High-Performance Computing (HPC) and quantum cloud services, place a premium on the ability to standardize deployment protocols across disparate hardware backends. Without a coordinated infrastructure, the reproducibility of quantum experiments is compromised, hindering the sector's ability to demonstrate consistent benchmarking and performance metrics. Consequently, the maturation of DevOps practices within companies like Qblox is a primary determinant of whether the ecosystem can sustain the transition from research-heavy pilots to scalable, multi-tenant quantum services.
Strategic focus within the value chain has moved toward hardware abstraction and the modularization of the software stack. This requires a sophisticated management of the software-hardware interface to ensure that emerging algorithms can be compiled and executed with high fidelity across heterogeneous systems. Current industry focus lies on bridging classical and quantum capabilities at scale, necessitating infrastructure that can handle the parallelization of research initiatives while maintaining the stability of core technology assets. As public-private funding cycles demand higher accountability for TRL progression, the implementation of rigorous configuration management and automated testing becomes the primary mechanism for maintaining technological momentum and reducing time-to-market for quantum-enabled solutions.
The capability architecture for this role type centers on the synchronization of cloud-native orchestration with specialized hardware development lifecycles. Mastery of infrastructure-as-code and containerization layers is essential for ensuring that complex build environments remain consistent across local, edge, and cloud deployments. This requires a deep understanding of the integration points between high-level application programming interfaces and the underlying Linux-based control systems that manage real-time quantum gate executions. Such capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of FPGA and embedded software development without introducing architectural fragmentation.
By establishing deterministic CI/CD pipelines for non-standard hardware targets, this function provides the leverage needed to accelerate the iteration frequency of quantum control software. These structural layers of expertise reduce the operational overhead associated with managing multi-cloud and hybrid-HPC environments, which is critical for long-term interoperability within the emerging quantum-as-a-service market. Furthermore, the integration of secure governance and compliance frameworks ensures that the scientific outputs are reconciled with the practical constraints of enterprise data sovereignty. Such expertise minimizes the "translation gap" between abstract research and product delivery, securing the foundation for long-term ecosystem readiness. - Accelerates the deterministic transition from laboratory research to scalable quantum control infrastructure
- Mitigates systemic execution risks by automating the deployment of complex hardware-software control stacks
- Facilitates the integration of quantum systems into established high-performance computing and cloud-native frameworks
- Strengthens the reliability of organizational technology roadmaps through standardized configuration management protocols
- Reduces iteration friction between low-level firmware updates and high-level software application development
- Optimizes the throughput of R\&D teams by eliminating manual bottlenecks in the build and test lifecycle
- Enhances the stability of the quantum value chain by providing predictable architectural requirements for partners
- Supports the scaling of quantum processors by managing the orchestration of heterogeneous computational resources
- Improves the transparency of technology readiness level progression for institutional and private investors
- Enables the structural reproducibility of quantum experiments through automated environmental auditing and logging
- Protects high-capital R\&D investments by ensuring alignment between engineering practices and scientific objectives
- Orchestrates the convergence of classical DevOps methodologies with the unique constraints of quantum hardwareIndustry Tags: Quantum Control Systems, DevOps Engineering, CI/CD Automation, Infrastructure as Code, FPGA Development Lifecycles, Hybrid Quantum-Classical HPC, Embedded Systems Orchestration, Deep Tech Scalability, Technology Readiness Level Advancement
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