About Pasqal
PASQAL designs and develops Quantum Processing Units (QPUs) and associated software tools.
Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs.
In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.
Job description
The Quantum Processing Unit (QPU) is operated by our specific Control Software PasqOS (running on Linux). This role is part of the PasqOS team and involves working with researchers and physicists working on the Quantum Processing Units as well as other people from the Hardware department: which implies interacting with the maintenance team, operations team in charge of cloud services and scientists developing various other software components to be integrated. Of course, no need to be a Quantum Physics PhD, we already have a lot of them, and the cool thing is : they explain very well!
As Software engineer, your role will include:
- Implement new features, hardware drivers or software enhancements on PasqOS on various research QPUs.
- Deploy PasqOS on the Quantum Processing Units (QPU), including helping the research and deployment team for tuning and testing.
- Investigate potential problems with the QPU System and Software teams (the System team works on tuning and optimizing the QPU hardware, they need support and often new features from PasqOS team).
- Help the integration of PasqOS with other components of the Pasqal Software ecosystem (cloud services, Quantum libraries...).
- Suggest and implement tools to enhance the deployment and upgrades of the QPUs.
- Evaluate the performance of PasqOS and suggest improvements and optimizations.
About you
- You have a master's degree or an engineering degree with a specialization in computer science, over 3 years of experience in software development and integration interfacing with hardware equipment.
- Your professional background has provided you with experience throughout the entire software development lifecycle.
- You demonstrate a genuine interest, in new technologies and scientific challenges.
- You have a strong ability to work in autonomy and you can take responsibility for your projects while collaborating with the rest of the team
The required technical skills for this position include:
- Proficiency in Python software development as well as some native language (C, C++ or Rust)
- Strong experience with version control (Git) and CI tools (GitLab)
- Interfacing with low level drivers.
- Experience with Hardware drivers.
- Proficiency with Linux (configuration, network, usb devices).
- Database management (PostgreSQL).
- Rust experience (or will to learn it) is a real plus.
- Front-end tech (react) is a plus.
- Proficiency in English and French (oral and written). The teams speaks french.
What we offer
- Beautiful brand new offices in Massy, France
- A flexible rhythm of remote work (2 to 3 days per week)
- Type of contract : CDI
- A key role in a growing start-up
Recruitment process
- An interview with our Talent Acquisition Specialist of 30'.
- An exchange with Engineering manager 45'.
- A meeting with the team in our beautiful offices 1h30.
- An offer!
PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The escalation of software integration engineers specializing in hardware abstraction layers represents a critical pivot in the quantum computing sector from experimental lab environments to stabilized, production-ready architectures. As the deep-tech ecosystem matures, the structural necessity for roles that bridge physical Quantum Processing Units (QPUs) and higher-level software stacks becomes paramount to resolving the critical translation gap between theoretical hardware capabilities and scalable cloud deployments. This role type serves as a high-leverage stabilization point within the system control layer, ensuring that core quantum operating software is reliably integrated with complex instrumentation and external cloud fabrics. Market signals from regional technology consortia and national quantum strategies highlight that specialized systems engineering is essential for mitigating the systemic risks of operational downtime in nascent high-performance computing facilities. By converting experimental scientific hardware inputs into deterministic, robust software-hardware interfaces, this function secures the operational foundation for long-term industrial access and commercial scalability across the deep-tech value chain.
The quantum computing landscape is undergoing a decisive shift from pure physics validation to the integration of high-fidelity control software within heterogeneous classical-quantum environments. While physical qubit development continues to progress across multiple hardware modalities, the primary bottleneck for continuous uptime has shifted to the systems software layer, specifically regarding the reliability of hardware-driver orchestration and operational runtime environments. Current industry focus lies on bridging classical and quantum capabilities at scale, necessitating a highly structured management of the software-hardware interface to ensure that hybrid workflows can survive the rigorous data throughput and timing requirements of production environments.
Workforce dynamics are notably complex at the intersection of low-level software engineering and industrial automation. As organizations advance past preliminary technology benchmarks, the ecosystem requires specialized software architects who can navigate the fragmentation of emerging hardware control APIs and the lack of unified communication protocols between physical subsystems. Current industry trends, influenced by strategic public funding cycles and cloud-delivery mandates, place an significant premium on software roles that can drive absolute interoperability across disparate operating environments. This structural layer of expertise acts as the primary mechanism for stabilizing software deployments as deep-tech hardware moves through sequential Technology Readiness Levels (TRLs).
Furthermore, the integration of physical QPUs with high-performance computing (HPC) frameworks remains a major structural dependency for the entire sector. The evolution of the commercial value chain depends on the ability to expose quantum hardware to external software ecosystems without disrupting established data center infrastructures or suffering severe latency overheads. Consequently, the availability of integration engineers capable of managing these intricate cross-functional dependencies between hardware departments, operations teams, and cloud providers is a primary determinant of whether a commercial organization can successfully transition from isolated laboratory prototypes to distributed quantum-as-a-service (QaaS) models.
The capability architecture for this software integration role type centers on the synchronization of low-level systems programming with the protocol frameworks of enterprise-grade systems engineering. Mastery of the hardware-software interface layer is essential for ensuring that specialized control software is fully optimized for the deterministic timing constraints and real-time execution parameters of quantum hardware controllers. This requires a sophisticated configuration capability spanning low-level operating system network stacks, peripheral device communications, and high-performance kernel interaction protocols.
These capabilities are fundamental to the operational throughput of deep-tech organizations, as they enable the parallelization of experimental physics research alongside the development of stable cloud delivery layers. By establishing rigorous automation and continuous deployment frameworks, this function provides the operational leverage needed to validate hardware performance before exposing systems to wider software layers. Furthermore, the ability to architect clean abstraction layers ensures that scientific breakthroughs within the hardware core can be translated into reliable software libraries without requiring downstream application developers to possess deep domain knowledge of physical subcomponents. - Accelerates the deterministic transition from experimental laboratory setups to production-ready quantum computing environments
- Mitigates systemic operational risks by synchronizing rapid hardware iterations with near-term software release cycles
- Facilitates the seamless integration of physical quantum processing units into standardized high-performance computing networks
- Strengthens the reliability of core system operations through the implementation of automated deployment protocols
- Reduces integration friction between low-level hardware control drivers and high-level application programming interfaces
- Optimizes the performance of physical processing kernels by maintaining stable and scalable runtime environments
- Enhances the resilience of the system stack by diagnosing complex faults across hardware-software boundary lines
- Supports the parallel advancement of multi-disciplinary teams by creating clean, stable technology abstraction layers
- Improves the predictability of system upgrades through the development of robust configuration management tools
- Enables the systematic reproducibility of hardware benchmarking data via stabilized data collection pathways
- Protects capital-intensive infrastructure investments by ensuring continuous software compatibility with evolving hardware baselines
- Orchestrates the technical convergence of high-performance cloud distribution layers with real-time physical control architecturesIndustry Tags: Quantum Computing Software, Hardware-Software Integration, Systems Engineering, Low-Level Drivers, High-Performance Computing, Deep Tech Infrastructure, Linux Systems Programming, Embedded Automation, Quantum Operating Systems
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