Quantum Solutions Engineer (Mid‑Senior)

About the role

We are seeking a Mid‑Senior Application Engineer specialized in quantum optimization to join our team. You will work on client projects that translate real‑world problems into quantum‑ready optimization formulations and deliver solutions on Pasqal neutral‑atom quantum processors.

Key responsibilities

• Execute quantum‑optimization projects from problem framing to delivery.
• Support the Commercial team with feasibility studies and scope definition.
• Translate real‑world challenges into combinatorial optimization models and evaluate solution strategies.
• Research state‑of‑the‑art academic and industrial literature to identify feasible research directions.
• Develop, scale, and benchmark quantum optimization solutions on Pasqal hardware and HPC emulation backends.
• Design hardware‑aware implementations using analog neutral‑atom paradigms.
• Define blueprints of quantum utility for client use‑cases and collaborate with R&D hardware teams.
• Collaborate with internal and external stakeholders throughout project phases.
• Contribute to team knowledge sharing and scientific watch activities.

Required profile

• MSc or PhD in Combinatorial Optimization, Quantum Computing or a related field.
• At least 3 years of experience in a similar role.
• Strong background in combinatorial optimization (linear programming, constraint programming, heuristics, meta‑heuristics, graph theory).
• Deep understanding of quantum computing concepts (quantum algorithms, Hamiltonian‑based optimization, Ising formulations, adiabatic and variational approaches, hardware constraints of neutral‑atom QPUs).
• Ability to connect theory with practical applications on current and future quantum hardware.
• Delivery mindset: prototype, benchmark, iterate and produce clear technical reports.

Required skills

• Python programming with good software‑engineering practices (version control, testing, documentation).
• Linear programming and constraint programming.
• Heuristics, meta‑heuristics, graph theory.
• Quantum algorithms, Hamiltonian‑based optimization, Ising models.
• Adiabatic and variational quantum approaches.
• Experience with neutral‑atom quantum processors and hardware‑aware implementation.
• Algorithm evaluation and benchmarking on HPC or emulation backends.