Quantum Solutions Engineer - Optimization or Machine Learning

About the Role

Join a leading global deep-tech firm specializing in neutral-atom quantum processors and associated software tailored for both industrial and scientific sectors. The company is expanding its footprint in Saudi Arabia by developing a local Quantum Applications team, with openings in two distinct expertise areas: Quantum Optimization and Machine Learning/Quantum Machine Learning. These roles prioritize applied, client-centric work over theoretical research, focusing on deploying quantum, classical, and hybrid strategies to address real-world industrial challenges and translate pioneering research into actionable solutions.

Key Responsibilities

• Convert complex industrial problems into mathematical formulations involving optimization or machine learning.
• Manage comprehensive customer projects from initial feasibility and technical discovery phases through development to final delivery.
• Develop and test solutions incorporating quantum, classical, and hybrid techniques using Python, emulators, high-performance computing resources, and quantum hardware.
• Establish dependable classical baselines and critically assess potential quantum advantages.
• Collaborate with a diverse team including clients, domain specialists, scientists, software developers, and hardware engineers.
• Create maintainable software, thorough technical documentation, and transparent results for clients.
• Take responsibility for meeting project milestones, managing technical risks, and ensuring timely deliverables.

Desired Expertise

Optimization Track:

• Deep knowledge in combinatorial optimization, operations research, or applied mathematics.
• Proficiency in mathematical modeling and algorithm creation.
• Experience solving scheduling, routing, logistics, graph, or resource allocation problems.
• MSc or PhD in a pertinent technical discipline plus a minimum of three years’ relevant work experience.
• Familiarity with QUBO, Ising models, or quantum optimization is a plus.

Machine Learning Track:

• At least five years of experience in machine learning or applied scientific fields.
• Strong capability in constructing complete end-to-end ML pipelines.
• Familiarity with graph machine learning techniques, graph neural networks, or equivalent methodologies.
• Proven success in transitioning research into production and conducting model benchmarking.
• Candidates with strong ML, mathematical, and engineering skills can receive training in quantum technologies; prior quantum or physics exposure is advantageous but not essential.

Common Requirements for Both Tracks:

• Excellent Python programming skills alongside strong software engineering principles.
• Experience in algorithm evaluation and benchmarking.
• Ability to integrate theoretical models with practical, real-world applications.
• Confidence in engaging directly with clients and collaborating with international technical teams.
• Professional proficiency in English.
• Commitment to working from the Riyadh office two to three days per week.

Additional Advantageous Skills

Experience in energy sector, logistics, industrial optimization, high-performance computing, or advanced computational fields will be beneficial. Knowledge of Arabic is helpful but not mandatory. There is potential for an initial hands-on training period in France with technical teams, depending on candidate background.