Machine Learning Engineer

Summary

Quantum Machines is a global leader in quantum computing control systems, developing hardware and software solutions for building and controlling quantum computers. The Machine Learning Engineer will design, build, and deploy machine learning systems for quantum processor calibration, control, and operation, including reinforcement learning, Bayesian inference, and agentic frameworks. The role also involves integrating ML services with Quantum Machines' control stack and collaborating with customers, partner labs, product, R&D, and hardware teams.

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

Skills

• 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 (architecture, Git workflows, testing, code review)
• Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer 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 - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
• Experience with sim-to-real, multi-objective RL, or meta-learning- advantage

Qualifications

Must Haves

• 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 (architecture, Git workflows, testing, code review)
• Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer 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

Nice to Haves

• Familiarity with quantum computing concepts - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
• Experience with sim-to-real, multi-objective RL, or meta-learning- advantage