Quantum Systems Software Engineer II

Quantum Software; Redmond (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Quantum Software in Redmond, WA seeks a systems software engineer to design, implement, and maintain software tools for quantum hardware control and data analysis. You will write high‑quality Python and system languages (C/C++/C#/Rust), build Qt-based desktop tooling, and collaborate with physicists and hardware engineers to translate complex requirements into reliable software.

The role emphasizes solid software engineering practices, automated testing, and scalable architectures in an

Qualifications

• Master's or Bachelor's in Physics/Engineering with 2+ years in industry or R&D, or equivalent.
• 2+ years programming in Python and at least one system programming language (C, C++, C#, Rust).
• Experience developing desktop or system tooling using Qt and collaborating across hardware/software boundaries.

Responsibilities

• Design, implement, and maintain software tools for quantum hardware control and operation.
• Develop high-quality Python and system languages (C/C++/C#/Rust).
• Build Qt-based desktop tooling to enable efficient workflows.
• Collaborate with physicists and hardware engineers to translate requirements into reliable software.

Skills

Python

C/C++

Qt

Rust

Automated testing

Team collaboration

Education

Master's Degree in Physics or Engineering

Bachelor’s Degree in Physics or Engineering + 2+ years experience

Tools

Qt

Git

Job description

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 Research & Development (R&D) environment. Contribute to code reviews, design discussions, and continuous improvement of engineering practices.

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. Ability to leverage AI tools to drive innovation and efficiency (e.g., performance modeling and analysis, research gathering, day‑to‑day task automation) Ability to work in an “AI‑first” environment using modern AI tools to accelerate discovery through hardware development Proficient understanding of software engineering fundamentals, including object‑oriented design, proper software factoring, and maintainable system design. 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. Interest in quantum computing, advanced hardware systems, or large‑scale experimental platforms. 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.

TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs

The emergence of fault-tolerant quantum computing necessitates a fundamental shift from experimental lab scripts to industrial-grade systems engineering. This role type serves as the vital bridge between theoretical physics and operational hardware, ensuring that complex qubit architectures are accessible through reliable software control layers. By stabilizing the interface between classical and quantum domains, these engineers directly influence the technology readiness level (TRL) of the entire sector. Market signals indicate that as hardware matures, the primary bottleneck has transitioned from qubit counts to the software‑defined reliability of the control stack. This technical tier is structurally essential for translating raw research into scalable, cloud‑accessible quantum advantage.

Within the quantum value chain, the systems software layer functions as the primary orchestrator between low‑level hardware physics and high‑level algorithmic execution. As the industry moves toward the Noisy Intermediate‑Scale Quantum (NISQ) threshold and beyond, the complexity of managing hardware‑adjacent software has intensified. Macro‑level analysis suggests that the sector is currently facing a "translation gap," where the ability to maintain and calibrate quantum devices cannot keep pace with architectural breakthroughs. This necessitates a workforce capable of applying rigorous classical software engineering principles—such as modularity and maintainable codebases—to the inherently probabilistic nature of quantum systems.

The ecosystem is also navigating the integration of hybrid classical‑quantum workflows, requiring software that can handle ultra‑low latency exchanges between traditional GPUs and quantum controllers. While significant public and private funding has accelerated hardware development, the maturity of the supporting software ecosystem remains fragmented. Vendor‑neutral representations and standardized control interfaces are emerging but have yet to reach universal adoption. This fragmentation poses a risk to interoperability, making the role of systems software integration a strategic determinant of a platform's long‑term commercial viability.

Furthermore, the rise of "AI‑first" development environments is reshaping how quantum software is architected. Machine learning models are increasingly deployed to automate calibration, optimize circuit gate depth, and mitigate environmental noise. These trends reflect a broader move toward autonomous quantum operations, where software agents manage the intricate physical constraints of the hardware. This evolution ensures that quantum systems can operate with the high availability required for integration into global supercomputing networks.

The capability architecture for this role type centers on the intersection of systems programming and hardware instrumentation. Foundational expertise in high‑performance languages like C++ or Rust is paired with Python‑based frameworks to facilitate rapid iteration in R&D environments. These capabilities are critical for developing the control stacks that generate and monitor the analogue signals necessary for qubit readout. By implementing modular software architectures, engineers ensure that the control layer remains decoupled from the specific physical implementation of the qubits, whether they are superconducting, trapped ion, or photonic.

Beyond core programming, the skill set encompasses the design of hardware‑adjacent tooling and user‑facing applications that enable physicists to interact with complex experimental platforms. Mastery of cross‑platform UI frameworks like Qt and robust automated testing protocols is essential for reducing the manual effort traditionally associated with device calibration. This structural enablement allows for faster debugging across the software‑hardware boundary, directly shortening the feedback loop between theoretical modeling and physical validation. Such a coupling is vital for ensuring the reliability of performance‑sensitive systems and long‑running services in the quantum cloud.

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

Industry Tags: Quantum Systems Engineering, Software Control Stacks, Hybrid Classical‑Quantum Integration, Hardware‑Software Co‑design, Quantum R&D, System Language Programming, Quantum Hardware Control, Scalable Quantum Architectures

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