Lead R&D, Quantum and AI

Join Qutwo - Europe's private AI Lab in the quantum era , 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 anexceptional R&D Leadto set the scientific direction of our AI and quantum computing platform. If you are a world-class researcher who also ships production-grade implementations, and you are ready to build and lead a team of brilliant scientists, this role is for you.

Role Description

As Lead R&D, you will lead the research team responsible for algorithms development, application discovery, and the scientific foundations of Qutwo OS. You will set the research strategy, ensure that research translates into deployable capabilities, and maintain Qutwo’s credibility in the global quantum computing and machine learning community through publications and conference presence. You report directly to the CTO and Head of Research.

This is not a pure management role. You are expected to conduct original research, write production code, and serve as the senior scientific authority for the team.

Qualifications

• A strong track record of high-quality research and innovation in quantum computing and ML/AI, demonstrated by publications at top-tier venues.
• Minimum 8 years of relevant post-PhD experience (or equivalent experience after an MSc).
• Proven experience with quantum-inspired and quantum algorithms, and the ability to benchmark them.
• Deep practical knowledge of one or more major quantum algorithm frameworks (Qiskit, Cirq, PennyLane).
• Deep practical knowledge of one or more major ML frameworks (PyTorch, TensorFlow, JAX).
• Practical experience with quantum and AI cloud platforms.
• Demonstrated ability to take research from paper to production-grade implementation.
• Experience building, leading, or mentoring a research team.
• 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.
• Quantum machine learning (QML), including parameterized quantum circuits or quantum kernel methods.
• Familiarity with quantum circuit optimization, error correction, or error mitigation techniques.
• Industry experience applying AI and/or quantum computing to optimization, finance, energy, or logistics.