(Junior & Senior) Quantum Engineer - Quantum Machine Learning

We are looking for a quantum engineer to join our Technology team. In this role, you will work on three main topics: developing QML quantum algorithms, packaging of quantum solutions into callable services for our client platform and/or client projects, exploring encodings and use cases of industry problems for current quantum computers. You will work in collaboration with other quantum algorithm engineers and software developers and your work will integrate into the productization of the company's tech.

Responsibilities

• Contribute to the design and implementation of quantum machine learning algorithms
• Support the development and benchmarking of QML models on simulators and real quantum hardware
• Improve the engineering of quantum algorithms, circuit depth reduction, encoding of problems, and implementation on hardware.
• Package quantum solutions towards delivery on our client platform
• Maintain the existing services and other delivered client services
• Continuously improve the productization process of the quantum solutions

Qualifications

• Master's or Ph.D degree in Quantum Computing, Computer Science, Mathematics, or a related field.
• Strong experience with quantum algorithms and quantum computing libraries.
• Strong experience in Python language.
• Experience with Git and CI-CD Pipeline.
• Experience with code development at a professional level
• Strong problem-solving skills and the ability to work in a cross-disciplinary team.
• Nice to have Understanding of quantum error correction and noise mitigation strategies.
• Experience with classical ML and optimization techniques and their quantum counterparts.

Benefits

• A mission at the frontier of quantum computing, alongside world-class scientists and engineers
• A role with real ownership
• An international team based in Berlin
• Contributions toward public transport or shopping vouchers
• International remote work opportunities
• 30 vacation days
• Adaptable working hours and hybrid-friendly options