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
At PASQAL, we build neutral-atom Quantum Processing Units (QPUs) where lasers, control systems and software meet cutting-edge physics. In the Quantum Register team, we develop algorithms that make qubit preparation scalable, fast, and reliable, a cornerstone for quantum simulation and error correction.
We’re looking for experienced scientific software / algorithms engineer to take ideas from theory → simulation → experimental validation → production-grade software that directly improves register performance.
Main responsibilities
The ideal candidate will have a strong interest in developing and implementing algorithms that apply concepts from math, physics, statistics and engineering concepts to solve multi-disciplinary problems at the hardware and system level.
What you’ll do
System-level problem solving
- Convert hardware constraints specifications and physical principles into algorithmic architectures, interfaces, and functional block diagrams.
- Build a strong understanding of one of Pasqal’s algorithm library and how it connects to the wider software stack and hardware systems.
R&D scientific software development & validation
- Explore algorithmic approaches through simulation, data analysis, and quantitative benchmarking.
- Run literature reviews / technology watch to bring relevant methods into the team.
- Define metrics, validate performance, and write clear test reports.
Performance engineering
- Profile and optimize runtime and resource usage (CPU/GPU when relevant).
- Deliver reliable, scalable implementations with solid engineering practices.
- Interface solutions with lab/industrial instrumentation when needed.
Collaboration & communication
- Work closely with software and hardware engineers to integrate algorithms from end-to-end.
- Present results to mixed audiences and document methodologies and architecture.
What we are looking for
- Ideally 5+ years of experience managing, building and deploying scientific libraries, from R&D to production.
- Strong Python + software engineering best practices (testing, CI mindset, code quality).
- Foundations in applied math/physics (numerical methods, statistics, optimization, modeling).
- Bonus: C/C++/Rust, GPU/vectorization, ML experience.
- Clear written and spoken English (French is a plus).
- Ability to operate with autonomy, prioritize, and collaborate in multidisciplinary teams.
What we offer
- Brand new offices in Massy
- A flexible rhythm of face-to-face work (2-3 days of telecommuting per week)
- Type of contract : CDI (permanent contract)
- A dynamic and close-knit international team
- A key role in a growing scaleup
- Free time to train and go to conferences/meetups
Recruitment process
- An interview with our people acquisition team of 30'.
- An exchange with your futur manager
- A technical use case
- A meeting with the team in our offices
- 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 emergence of senior developers specializing in hardware-level quantum algorithms represents a critical shift in the deep-tech sector from theoretical physics to scalable systems engineering. As the quantum computing market transitions through varying Technology Readiness Levels, stabilizing the physical qubit registration layer is paramount to achieving practical quantum simulation and error correction. This role type serves as a primary translation pathway, transforming abstract mathematical logic into deterministic control software that interfaces directly with physical hardware architectures. Market signals from global consortiums indicate that optimizing physical register performance is a primary determinant of commercial readiness for full-stack deep-tech providers. By bridging the critical gap between hardware physical constraints and high-level software abstractions, this function secures the performance predictability required for enterprise-scale computational deployment. Consequently, this structural layer of expertise protects capital-intensive research investments by converting fragile quantum states into highly reliable processing units.
The quantum hardware landscape is undergoing a decisive transition where the primary engineering bottleneck has shifted from basic qubit isolation to the scalability and reliability of control mechanisms. In neutral-atom systems and related modalities, the optimization of initial qubit preparation and register stabilization forms the foundational layer upon which all subsequent algorithmic fidelity depends. Current industry focus lies on bridging classical and quantum capabilities at scale, necessitating sophisticated mathematical modeling of physical hardware constraints to eliminate execution friction before higher-level compilation occurs.
Sector-wide observations highlight that workforce scarcity is acute at the specific intersection of low-level embedded software engineering and experimental quantum physics. Organizations across the global ecosystem require specialized engineers who can navigate the complex coupling between hardware instrumentation and automated system calibration. This tension is magnified by the fragmentation of the quantum software stack, where a lack of standardized benchmarking frameworks forces developers to build bespoke verification loops for unique physical platforms.
Furthermore, macroeconomic factors such as shifting public funding cycles and national technology sovereignty mandates place a premium on roles that directly advance hardware interoperability and energy-efficient operations. As deep-tech infrastructure integrates with existing high-performance computing centers, the translation of theoretical gate performance into production-grade runtime efficiency is a major market dependency. The availability of senior architects capable of managing these system-level trade-offs directly dictates the velocity of the commercial value chain.
The capability architecture for this role type centers on the synthesis of applied mathematics, numerical optimization, and production-grade software engineering protocols. Mastery of low-level hardware interfaces and instrumentation logic is essential to ensure that algorithmic architectures are structurally compatible with physical constraints like coherence times and control-signal delays. This requires establishing rigorous data validation loops that leverage statistical modeling to analyze system-level data and optimize execution runtimes.
These technical domains are fundamental to the throughput of full-stack deep-tech organizations because they enable the parallelization of hardware development alongside algorithm library expansion. By creating high-fidelity simulation environments, this function allows for the quantitative benchmarking of control loops prior to physical implementation on active processors. This continuous integration mindset reduces iteration friction between quantum information theory and practical industrial application. - Accelerates the transition of quantum registration concepts from theoretical models to production-grade control libraries
- Reduces execution friction at the hardware-software interface through automated multi-disciplinary optimization protocols
- Mitigates system-level stability risks by establishing rigorous algorithmic verification and performance validation frameworks
- Enhances physical qubit preparation velocity to support complex quantum error correction topologies
- Facilitates closer cross-functional coupling between experimental physics teams and enterprise software developers
- Optimizes computational resource utilization across hybrid classical-quantum infrastructure benchmarking environments
- Stabilizes the deep-tech value chain by translating hardware constraints into deterministic functional block diagrams
- Lowers iteration development cycles through predictive simulation and quantitative hardware-level profiling
- Strengthens organizational technology strategies by implementing reproducible testing methodologies for control software
- Supports the scalable deployment of quantum processing units within global supercomputing centers
- Protects capital-intensive infrastructure investments by maximizing the operational fidelity of physical processors
- Orchestrates the convergence of low-level instrumentation control with high-level scientific software librariesIndustry Tags: Quantum Hardware Optimization, Neutral-Atom QPUs, Embedded Control Algorithms, Scientific Software Engineering, Qubit Register Stabilization, Deep Tech Integration, Performance Engineering, Numerical Optimization
Keywords:
NAVIGATIONAL: Pasqal quantum hardware engineering careers, Pasqal algorithm developer jobs, Pasqal Quantum Register team vacancies, Pasqal Massy office engineering roles, Pasqal scientific software developer hiring, Pasqal quantum processor control careers, Pasqal full stack quantum engineering
TRANSACTIONAL: apply for senior quantum algorithm roles, hiring quantum hardware software engineers, senior scientific developer vacancies in France, apply for hardware algorithm developer positions, professional quantum control engineering careers, senior python scientific library jobs, apply for neutral atom quantum roles
INFORMATIONAL: neutral atom quantum register architecture, optimizing qubit preparation algorithms, hardware constraints in quantum simulation, role of control software in quantum processing, benchmarking quantum control loop performance, python software practices for scientific research, quantum error correction hardware requirements
COMMERCIAL INVESTIGATION: leading companies for neutral atom quantum computing, best hardware algorithm development teams, comparing quantum control software strategies, top quantum processing unit manufacturers, evaluating quantum register performance tooling, career paths for scientific software engineers
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.
