Machine Learning Engineer

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Quantum Machines

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏭 Manufacturing

🚀 Aerospace

Consulting • Manufacturing • Aerospace

Quantum Machines is a leading company in quantum computing, focusing on the development of technologies to accelerate the practical implementation of quantum computers. Their Quantum Orchestration Platform (QOP) is designed to harness the full potential of quantum technologies, enabling quantum system developers to achieve advanced capabilities. With a team of world-class quantum scientists and engineers, Quantum Machines aims to revolutionize various industries through innovative quantum solutions.

📋 Description

• Design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors • Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement 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 integrating with the 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 with product, R&D, and hardware teams on internal libraries, customer-facing SDKs, and training materials

🎯 Requirements

• PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field • 4+ years of relevant experience • Strong background in Machine Learning and Deep Learning, with hands-on experience in at least one of: deep learning, reinforcement learning, agentic AI • Strong Python proficiency, including scientific or systems-oriented codebases • Solid software engineering fundamentals, including architecture, Git workflows, testing, and code review • Proven track record of taking ML from prototype to deployment under real-world constraints, including non-stationary data, expensive evaluations, or safety-critical action spaces • Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML experience transfers well • Strong problem-solving skills and customer-focused mindset • Ability to work independently and in multidisciplinary teams • Proven software development track record and excellent technical communication skills • Familiarity with quantum computing concepts, including qubit calibration, randomized benchmarking, QEC, and optimal control • Experience with sim-to-real, multi-objective RL, or meta-learning is an advantage