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Quantum Machines develops technologies for practical quantum computing, with a focus on the control systems that help researchers and engineers operate advanced quantum hardware. Its Quantum Orchestration Platform brings together the tools needed to program, manage, and optimize quantum experiments, supporting work from system development through implementation. The company’s team of quantum scientists and engineers works on infrastructure intended to move quantum technologies from research environments toward real-world applications.
Machine Learning Engineer, Quantum Control (Hybrid, Germany)
Build and deploy machine learning systems for quantum processor calibration, control, and operation. Apply reinforcement learning, Bayesian inference, and Python services to real hardware in production laboratories.
Description
- Create and deploy machine learning systems that enhance quantum processor calibration, control, and operation
- Convert noisy, changing, safety-critical control challenges into machine learning solutions for production laboratory hardware
- Develop reinforcement learning policies, Bayesian inference techniques, and agentic frameworks for autonomous, efficient, and drift-tolerant quantum control
- Create methods for parameter optimization, drift monitoring, and adaptive measurement on real hardware
- Implement real-time parameter steering for calibration during QEC and between circuits
- Build and maintain agentic frameworks that automate system control and calibration
- Build and maintain Python machine learning services and libraries that integrate with the Quantum Machines stack, including QUA, Qualibrate, and OPX1000
- Partner with customers and laboratory collaborators to deploy, validate, and refine machine learning solutions in experimental environments
- Work with product, R&D, and hardware teams on internal libraries, customer SDKs, and training resources
Requirements
- PhD or master’s degree in machine learning, physics, applied physics, quantum information science, or a related discipline
- At least four years of relevant professional experience
- Strong machine learning and deep learning expertise, with practical experience in deep learning, reinforcement learning, or agentic AI
- Advanced Python skills, including work with scientific or systems-oriented codebases
- Sound software engineering practices covering architecture, Git workflows, testing, and code review
- Demonstrated ability to move machine learning systems from prototype to deployment amid non-stationary data, costly evaluations, or safety-critical action spaces
- Transferable experience in robotics, online control, autonomous vehicles, or hardware-in-the-loop machine learning
- Strong analytical and problem-solving ability with a customer-oriented approach
- Ability to work effectively both independently and in cross-disciplinary teams
- Established software development record and excellent technical communication
- Knowledge of quantum computing concepts such as qubit calibration, randomized benchmarking, QEC, and optimal control is advantageous
- Experience with sim-to-real methods, multi-objective reinforcement learning, or meta-learning is advantageous
Benefits
- Work at the intersection of machine learning, quantum physics, and software engineering
- Gain exposure to varied qubit technologies and quantum architectures
- Use a direct feedback cycle between machine learning models and production laboratory hardware
- Deliver machine learning solutions for leading quantum systems laboratories and companies
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