Optimization Scientist/Engineer

Job description

Join Qutwo - Europe's private Quantum AI Lab , on our quest to bridge the gap between the classical and the quantum world. We partner with the most advanced and forward-thinking customers, solving fundamental real-world problems, side by side. The best scientists and engineers in the world are drawn to one thing: a problem worthy of their ability. At Qutwo, we have one. We are a European AI lab for the quantum era, co-founded by pioneers behind IQM and Silo AI, with a team spanning MIT, UC Berkeley, Cambridge, and Carnegie Mellon, and backed by the founders of Skype, Hugging Face, Quantinuum, Supercell and many more. We work at the intersection of quantum computing, AI, optimization, and simulation — where the answers do not yet exist and the work genuinely matters. We are seeking an exceptional Optimization Scientist/Engineer . If you excel at translating complex business problems into rigorous mathematical models and are passionate about benchmarking quantum and classical approaches, we encourage you to apply. Our main office is located in Helsinki, with established teams currently operating in Germany and Sweden. For the right candidate, we are fully open to discussing other remote or hybrid locations across Europe. Role Description As an Optimization Scientist/Engineer, you’ll work together with our team and customers full-time primarily in Finland, Sweden, or Germany. You will develop, implement, and rigorously benchmark optimization solutions — both classical and quantum — for real enterprise problems. You are expected to bring a blend of research excellence, practical implementation ability, and serve as a subject matter expert in optimization. Qualifications A strong track record of high-quality research and/or proven experience in optimization, demonstrated by publications or significant projects. Minimum 5 years of relevant post-PhD experience (or equivalent experience after an MSc). Strong background in mathematical optimization, operations research, or applied mathematics. Experience with QUBO formulations and/or quantum optimization approaches (QAOA, VQE, quantum annealing). Practical knowledge of one or more major quantum algorithm frameworks (Qiskit, Cirq, PennyLane). Rigorous benchmarking methodology — you understand when quantum provides genuine advantage and when it does not. Strong programming skills in Python. Ability to work collaboratively and communicate effectively across disciplines, both internally and externally. Experience in one or more of the following areas is a plus but not a requirement: Experience with hybrid quantum-classical architectures, including classical preprocessing or circuit simulation. Familiarity with quantum circuit optimization, error correction, or error mitigation techniques. Industry experience in optimization for finance, energy, logistics, or supply chain. Background in game theory or operations research.