Quantum Machines (QM) is a global leader in quantum computing control systems. We specialize in pioneering hardware and software solutions that leverage instruction-based quantum control to revolutionize the construction and operation of quantum computers. As the field of quantum computing experiences exponential growth, QM is committed to building an elite team that contributes to and shapes the future of quantum technologies.

About the Position

Introduction

Quantum Machines (QM) is a global leader in quantum computing control systems, revolutionizing how quantum computers are built and controlled through pioneering hardware and software solutions based on instruction-based quantum control. We are assembling an elite team to actively shape the evolution of quantum technologies.

We are seeking a Machine Learning Engineer to design, build, and deploy ML systems that enhance the calibration, control, and operation of quantum processors. This role involves working at the intersection of machine learning, quantum physics, and software engineering, transforming complex control problems into ML solutions for real hardware in production labs. You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks to make quantum control more autonomous, sample-efficient, and robust to drift. This position offers unparalleled exposure to diverse qubit types and quantum architectures, with a direct feedback loop between models and hardware, delivering groundbreaking ML-driven solutions to labs and companies defining the next generation of quantum systems.

Responsibilities

Develop reinforcement learning, Bayesian inference, and probabilistic modeling 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.

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 (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. Experience in robotics, online control, autonomous vehicles, or hardware-in-the-loop ML is highly relevant.
Strong problem-solving skills and a customer-focused mindset; ability to work independently and in multidisciplinary teams.
Proven software development track record and excellent technical communication skills.

Nice to Have

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