Quantum Systems Software Engineer — AI Tools & Control

Job Summary

Design, implement, and maintain software tools and applications that support configuration, control, and operation of quantum hardware systems. Develop high‑quality, well‑factored software in Python and system languages such as C, C++, C#, or Rust. Build and maintain user‑facing applications and tooling using Qt to enable efficient workflows for quantum hardware development and operations.

Apply solid software engineering fundamentals, including object‑oriented design, modular architectures, and maintainable codebases. Write robust unit, integration, and system tests to ensure correctness and reliability of critical tooling.

Collaborate closely with physicists, hardware engineers, and other software teams to translate complex system requirements into practical software solutions. Debug and resolve issues across software, system, and hardware boundaries in a fast‑moving R&D environment.

Contribute to code reviews, design discussions, and continuous improvement of engineering practices. Ability to leverage AI tools to drive innovation and efficiency, and to work in an “AI‑first” environment using modern AI tools to accelerate discovery through hardware development.

• Master’s Degree in Physics, Engineering, or related field OR Bachelor’s Degree in Physics, Engineering, or related field AND 2+ years experience in industry or in a research and development environment OR equivalent experience.
• 2+ years programming experience in Python and at least one system programming language (e.g., C, C++, C#, Rust).
• 1+ year(s) experience working in a collaborative, team‑based software development environment.
• Experience designing and writing automated tests and debugging non‑trivial software systems.
• Familiarity with Git and modern development workflows.
• Proficient written and verbal communication skills.
• Experience developing desktop or system tooling using Qt.
• Experience working on hardware‑adjacent software, instrumentation control, or systems that interact with physical devices.
• Familiarity with scientific or experimental software environments.
• Exposure to performance‑sensitive systems or long‑running services.
• Ability to leverage AI tools to drive innovation and efficiency.

• Facilitates the transition from experimental laboratory prototypes to standardized commercial‑grade quantum computing systems.
• Reduces integration friction between classical supercomputing environments and emerging quantum processing units.
• Mitigates systemic risks associated with hardware‑software decoherence through robust control layer engineering.
• Strengthens the reliability of cloud‑accessible quantum platforms by implementing maintainable and well‑factored codebases.
• Accelerates the iteration cycle of hardware development through the creation of efficient R&D software workflows.
• Optimizes the utilization of limited quantum resources by shortening gate depth and improving execution fidelity.
• Harmonizes cross‑disciplinary collaboration between hardware physicists and software architects via standardized tooling interfaces.
• Protects capital‑intensive hardware investments by ensuring stable and deterministic operation of control systems.
• Supports the scaling of multi‑qubit architectures through automated calibration and performance monitoring protocols.
• Shortens the time‑to‑market for practical quantum applications by bridging the TRL gap in software maturity.
• Improves the reproducibility of quantum research results through standardized testing and validation frameworks.
• Enables the autonomous operation of future quantum processors through the integration of AI‑driven optimization loops.