Machine Learning Engineer

Machine Learning Engineer

Infleqtion is a global quantum technology company solving the world's most challenging problems. The company harnesses quantum mechanics to build and integrate quantum computers, sensors, and networks. From fundamental physics to leading edge commercial products, Infleqtion enables "quantum everywhere" through our ecosystem of devices and platforms. Our mission is to commercialise atom-based quantum products that provide orders of magnitude improvements in performance and computing applications.

Infleqtion has offices in the USA, United Kingdom and Australia. This is a full-time position in our Kidlington and Harwell offices. Our flexible working policy enables all full-time employees to work up to 2 days a week from home as work permits.

Infleqtion is seeking a Machine Learning Engineer to develop advanced ML-driven applications that enhance the performance, stability, and sensitivity of our quantum technologies. In this role, you will design and deploy models for signal processing, noise suppression, drift compensation, anomaly detection, and adaptive calibration, working directly with experimental and hardware teams to translate complex physical system data into actionable improvements. You will build robust data pipelines, train and validate models on high-dimensional time-series and experimental datasets, and integrate inference capabilities into real-time or near-real-time control environments. The ideal candidate combines strong expertise in applied machine learning and scientific computing with experience in production-grade software systems and is motivated to apply modern ML techniques to push the limits of quantum technologies.

The duties and responsibilities outlined below include essential functions of the role. Depending on business needs, this role may perform a combination of some or all of the following duties. Duties, responsibilities, and activities may change, or new ones may be assigned at any time.

• Develop and implement custom machine learning models for a variety of tasks in the domains of signal processing and sensor fusion
• Stay up-to-date on literature, identify and pursue cutting edge model development techniques for low SWaP deployment
• Communicate and interpret cutting-edge research in both theory and experiment with colleagues
• Engage with the broader scientific community by publishing research findings in scientific journals and presenting at conferences
• Stay up to date with the latest developments in machine learning and sensor fusion
• Provide guidance and support to other team members, fostering a collaborative research environment