We are looking for a Research Engineer in Quantum Optimization with a strong background in Operations Research and experience in Quantum Computing to strengthen our team.
What you’ll do :
- Lead quantum optimisation R&D projects end-to-end within the Quantum Graph Optimization (QGO) team: from early technical discussions and problem framing to project delivery and handover.
- Investigate and synthesize the state of the art (academic and industrial literature) to identify relevant directions, assess feasibility, and propose impactful research paths.
- Develop and scale quantum optimization use cases on Pasqal quantum processors: improve and extend existing use cases ; identify, evaluate, and develop new ones, with a focus on combinatorial optimization.
- Translate real-world problems into optimization formulations and evaluate solution strategies using a mix of analytical reasoning and numerical experimentation.
- Design and run benchmarking and feasibility studies, including assessing the limitations of classical approaches and identifying realistic pathways toward quantum advantage.
- Develop solutions and experiment pipelines in close collaboration with the software engineering team, using emulation backends locally and on HPC when relevant.
- Work hardware-aware: investigate realistic implementations on neutral-atom hardware (analog and digital paradigms), including parameter constraints, noisy emulations, and practical limitations (e.g., addressability, shot-rate).
- Define blueprints for quantum utility experiments and collaborate closely with R&D hardware teams to bring them to fruition.
- Collaborate with internal and external stakeholders (Engineering, R&D, academic and industrial partners, and clients) throughout all phases of projects, ensuring alignment on technical scope, success criteria, and deliverables.
- Contribute to Pasqal’s intellectual property strategy, including identifying novel ideas and supporting invention disclosures and patent filings when appropriate.
- Support team-wide execution and knowledge sharing, including helping on code/components outside your direct ownership when needed and contributing to ongoing scientific watch activities.
- Monitor and respond to calls for project proposals, helping shape and coordinate Pasqal’s submissions and participation in collaborative R&D programs
About you
With a PhD in Combinatorial Optimization, Quantum Computing or in a related field and at least 3 years of experience after PhD, you have most of the following assets:
- Strong background in combinatorial optimization: familiar with classical problems, linear programming, constrained programming, heuristics, metaheuristics, complexity theory, graph theory
- R&D mindset: ability to explore, prototype, benchmark, and iterate on new approaches. Write clear technical reports and contribute to scientific publications
- Programming skills (Python): good software engineering practices such as version control, testing, and documentation
- Solid experience on algorithm evaluation & benchmarking: experiment design, reproducibility, performance analysis
- Able to collaborate with Engineering and R&D teams across different disciplines
- IP awareness: ability to identify patentable ideas and support IP drafting processes
- Project leadership: plan and drive projects end-to-end, manage milestones and risks
- Partner-facing skills: technical relationship management with industrial and academic partners
- EU project experience: monitoring and answering European calls for proposals, proposal writing, consortium work
- Knowledge about quantum computing, with interest in neutral-atom-based analog approaches. Experience in quantum optimization.
What we offer
- Contract type: Permanent contract based in Europe
- A dynamic and close-knit international team
- A key role in a fast-growing start-up
- Time allocated for training and attending conferences and meetups
Process de recrutement
- A 30-minute interview with our talent acquisition team
- A conversation with Wesley, the hiring manager
- A technical assessment
- Meet the team in the office
- Job offer!
Pasqal est un employeur garantissant l'égalité des chances. Nous nous engageons à créer un lieu de travail diversifié et inclusif, car l'inclusion et la diversité sont essentielles à la réalisation de notre mission. Nous encourageons les candidatures de tous les candidats qualifiés, quels que soient leur sexe, leur race, leur origine ethnique, leur âge, leur religion ou leur orientation sexuelle
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of Senior Quantum Algorithm Developers specializing in optimization represents a critical pivot in the quantum value chain from theoretical exploration to industrial utility. This role type serves as the primary technical interface between abstract operations research and the specific architectural constraints of near-term quantum hardware. By addressing the "optimization bottleneck"—a domain where classical high-performance computing increasingly faces scaling limits—these experts enable the translation of complex industrial variables into quantum-native formulations. Market signals indicate that the maturation of neutral-atom and superconducting modalities has created a high-demand tier for developers who can ensure algorithmic reproducibility while navigating current noise and coherence limitations. Consequently, this function is foundational for organizations aiming to secure first-mover advantages in logistics, finance, and material science.
The quantum computing industry is currently transitioning through a phase of "algorithmic hardening," where the focus has shifted from general-purpose discovery to domain-specific refinement. Within the broader ecosystem, optimization remains the most significant near-term application layer, particularly as global industries seek to resolve high-dimensional combinatorial problems that remain intractable for classical heuristics. However, the path to practical quantum advantage is constrained by a persistent disconnect between pure academic research and the engineering requirements of hardware-aware implementation. This TRL mismatch necessitates a specialized workforce capable of bridging the gap between high-level operations research and low-level pulse or gate-level constraints.
Macro-economic analysis of the quantum sector highlights that while capital investment in hardware remains robust, the primary risk to adoption is the "readiness gap" in the software stack. As hardware providers like Pasqal scale their qubit counts and improve fidelity, the industry requires a sophisticated tier of developers to build robust benchmarking frameworks. These frameworks are essential for establishing trust with end-users by providing transparent comparisons against state-of-the-art classical solvers like Gurobi or CPLEX. Furthermore, the integration of quantum kernels into existing High-Performance Computing (HPC) infrastructures represents a national strategic priority for major economies, driving demand for hybrid classical-quantum workflows.
Fragmentation across hardware modalities—spanning neutral atoms, trapped ions, and photonics—further complicates the software landscape. This fragmentation places a premium on developers who can maintain "hardware-awareness" while designing portable algorithmic components. As the ecosystem moves toward fault-tolerant regimes, the role of optimization experts in the Noisy Intermediate-Scale Quantum (NISQ) era remains vital for identifying the specific use cases that will yield the first instances of quantum utility. The ability to manage this complexity across multi-stakeholder R&D consortia is now a primary determinant of success for leading quantum firms.
The technical architecture for this role type centers on the convergence of combinatorial optimization, complexity theory, and quantum physics. At the foundational layer, mastery of operations research formulations is required to map real-world constraints into Ising models or Quadratic Unconstrained Binary Optimization (QUBO) structures. This proficiency is coupled with a deep understanding of NISQ-era algorithmic strategies, including Variational Quantum Algorithms (VQAs) and Quantum Approximate Optimization Algorithms (QAOA). These capabilities are essential for ensuring the structural throughput of R&D, as they allow for the systematic evaluation of circuit depth, parameter optimization, and error mitigation. Furthermore, the interface between quantum software and classical HPC backends requires expertise in emulation, distributed computing, and hybrid pipeline orchestration. This coupling ensures that quantum subtasks are not isolated experiments but integrated kernels within scalable enterprise architectures, directly influencing the stability and interoperability of the emerging quantum-classical stack.
Accelerates the deterministic progression of industrial optimization use cases toward practical quantum advantage
Mitigates systemic risks by establishing rigorous benchmarking protocols against state-of-the-art classical heuristics
Facilitates the seamless integration of quantum kernels into existing high-performance computing enterprise workflows
Reduces iteration friction in the development of hardware-aware algorithms for neutral-atom quantum processors
Strengthens intellectual property portfolios through the identification of novel quantum-native optimization formulations
Optimizes the resource lifecycle of hybrid classical-quantum systems by improving circuit efficiency and depth
Supports the scaling of quantum utility by identifying high-impact use cases in logistics and finance
Shortens the time-to-market for quantum-ready software solutions through standardized R\&D experiment pipelines
Improves the reliability of multi-stakeholder research initiatives by ensuring technical alignment on success criteria
Protects deep-tech investments by providing expert validation of algorithmic feasibility on current hardware
Enables the strategic orchestration of R\&D efforts across diverse academic and industrial partner networks
Harmonizes abstract operations research with the practical physical limitations of near-term quantum devices
Industry Tags: Combinatorial Optimization, Neutral Atom Quantum Computing, NISQ Algorithms, Operations Research, Hybrid Quantum-Classical HPC, Algorithmic Benchmarking, Quantum Advantage, Quantum Utility, R&D Strategy
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