Machine Learning Engineer

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.

You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.

Responsibilities:

Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
Develop real-time parameter steering for calibration during QEC and between circuits.
Develop and maintain agentic frameworks for autonomous system control and calibration.
Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.