About The Role and Team
Since 2021 our team has been listed every year in the “Top 100 Startups worth watching” in the EE Times, and our technology breakthroughs have been featured in The Telegraph, BBC and the New Statesman. Our founders are internationally renowned researchers from UCL and Oxford University who have pioneered the development of qubits and quantum computing architectures. Our chairman is the co-founder of Cadence and Synopsys, the two leading companies in the area of Electronic Design Automation. We’re backed by a team of top-tier investors including Bosch Ventures, Porsche SE, Sony Innovation Fund, Oxford Sciences Innovations, INKEF Capital and Octopus Ventures, and we have so far raised over £62 million in equity and grant funding.
We bring together the brightest quantum engineers, integrated circuit (IC) engineers, quantum computing theoreticians and software engineers to create a unique, world-leading team, working together closely to maximise our combined expertise. Our collaborative and interdisciplinary culture is an ideal fit for anyone who thrives in a cutting-edge research and development environment focused on tackling big challenges and contributing to the development of scalable quantum computers based on silicon technology.
Our team of 100+ is based across London, Oxford, San Sebastián and Sydney, with our primary
hub in Islington (London).
Our Team
The role will require the individual to maintain and develop in-house software stack for efficient data acquisition and analysis by hardware and integrated circuits teams within the company. This role will also include refactoring, writing and testing software which interfaces with test and measurement hardware. The role sits within the Intelligent Automation team, whose goal is to transform the automation of key parts of both the characterisation and operation of the quantum processor and its constituent parts. No background in quantum physics is required.
This is a rare and exciting opportunity to be an early employee at a start-up shaping the future of quantum computing. Being a small team and having a flat structure, this is a great opportunity to contribute to new developments within the field. There are vast opportunities for professional growth and to make an impact within the company.
Please note that this is a hybrid role, typically requiring 60% on-site attendance. Due to the nature of the role, a fully remote work pattern cannot be considered.
Functions of the Role
- Maintaining and expanding in-house software for experiment and instrument control
- Working with hardware experts to understand requirements, scope out and develop new features
- Improving code health for existing software libraries including refactoring, test development and documentation
- Managing the tracking of feature requests, issues and bugs to ensure their resolution
- Supporting best practice in the use of software repositories for measurement and analysis tools for use by members of the measurement teams
Experience - Essentials
- Minimum 2:1 degree in Computer Science or a closely related discipline
- Strong Python developer with at least 1 year of industry experience
- Solid understanding of object-oriented programming and core software design principles
- Strong analytical and problem-solving skills, with the ability to reason about complex systems
- Familiarity with Git, version control, and modern software development best practices
- A desire to work closely with end users in technical or experimental environments
- Strong collaborative approach with good communication and interpersonal skills
- Ability to work independently to achieve defined goals
Experience - Desirable
- Background in a physical science or engineering discipline (e.g. physics, engineering, applied mathematics)
- Experience using Python scientific and numerical libraries (e.g. NumPy, SciPy, Pandas, Matplotlib, Jupyter)
- Knowledge of additional programming languages (e.g. C/C++, Rust, MATLAB, or similar)
- Familiarity with software validation and testing practices
- Familiarity with preparing technical reports and presentations
Application Process
- Initial screening interview with Talent Team (20 mins)
- Technical Interview with Hiring Manager (30 mins)
- Take home coding exercise (2 – 3 hours of work, 1 week to complete)
- Interview with hiring manager + panel and 121’s with key stakeholders (2 hours)
Benefits
- Be part of a creative, world-leading team
- Competitive salary and share options scheme
- Contributory pension scheme
- Choose your own laptop/kit
- Life Assurance
- Cycle-to-work Scheme
- Flexible working
- Central London location
EEO Statement
Quantum Motion is committed to providing equal employment opportunity and does not discriminate based on age, sex, sexual orientation, gender identity, race, colour, religion, disability status, marital status, pregnancy, gender reassignment, religion or any other protected characteristics covered by the Equality Act 2010.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The transition from laboratory-scale quantum demonstrations to industrialized, fault-tolerant systems is fundamentally predicated on the maturity of the underlying software infrastructure. Research Software Engineers occupy a critical juncture in the quantum value chain, serving as the bridge between theoretical qubit architectures and robust hardware execution. By abstracting the complexities of device physics into reliable control and automation layers, this role type facilitates the high-throughput characterization essential for scaling silicon-based quantum processors. Current market signals, including the proliferation of national quantum strategies and increased venture capital flow into hardware-software co-design, underscore the structural necessity of engineering talent capable of maintaining code health in experimental environments. As the ecosystem moves toward the logical qubit era, the stabilization of measurement stacks directly dictates the speed of the hardware iteration cycle and the eventual commercial viability of universal quantum computers.
Within the global quantum ecosystem, the software layer is increasingly recognized as the primary bottleneck for hardware scalability. While fundamental physics research has established the feasibility of various qubit modalities, the sector now faces a significant "integration gap" between raw physical signals and actionable data. The Research Software Engineer role type addresses this by professionalizing the research environment, implementing modern software development practices where ad-hoc scripting previously dominated. This transition is essential for ensuring the reproducibility of results—a cornerstone for achieving the rigorous benchmarking standards required by institutional investors and government agencies.
Macro-level analysis suggests that the quantum hardware market is shifting away from isolated experiments toward modular, systems-level engineering. This evolution demands a sophisticated tooling layer that can interface with heterogeneous test and measurement equipment while providing a unified data acquisition framework. The complexity of silicon-spin qubit architectures, in particular, requires highly automated intelligent automation pipelines to handle the massive volumes of characterization data generated during the tuning process. Without a robust software backbone, the transition from physical prototypes to Logical Quantum Processing Units (LQPUs) remains gated by manual bottlenecks and high error rates in data interpretation.
Furthermore, public funding cycles and workforce development initiatives, such as those highlighted by the QED-C and national quantum programs in Europe and the UK, emphasize the urgent need for "quantum-literate" software engineers. These professionals enable the cross-functional coupling between IC design, cryogenic engineering, and quantum theory. As the industry matures, the ability to build scalable, extensible, and well-documented software libraries will be the primary determinant of whether a hardware architecture can successfully transition through the Technology Readiness Level (TRL) spectrum toward commercial deployment and hybrid classical-quantum cloud integration.
Capability domains for this role type are centered on the intersection of instrument control, automated characterization, and high-performance data processing. Mastery of object-oriented design and Python-based scientific ecosystems is foundational for building extensible libraries that can adapt to rapidly evolving hardware specifications. These capabilities matter because they provide the necessary leverage to automate complex measurement protocols, thereby reducing the time-to-insight for hardware teams. By implementing robust version control and automated testing for measurement software, this function mitigates the risk of regression in experimental setups, ensuring that hardware improvements are accurately captured and validated. Furthermore, the architecture of these software stacks must support interoperability between classical Electronic Design Automation (EDA) tools and quantum-specific control electronics, facilitating a seamless flow of information across the hardware-software interface. This structural enablement is a prerequisite for achieving the high-fidelity gates and long coherence times required for fault-tolerant operation.
Accelerates the industrialization of silicon-based quantum architectures through automated characterization pipelines
Reduces the technical debt inherent in rapid experimental hardware iteration cycles
Enhances the reliability of quantum processor benchmarking through standardized software protocols
Facilitates the transition from physical to logical qubits via robust measurement infrastructure
Minimizes integration friction between classical control electronics and quantum hardware layers
Optimizes hardware-software co-design by providing high-fidelity data acquisition frameworks
Strengthens the quantum supply chain by enabling modular instrument control libraries
Supports the scaling of qubit counts through the deployment of intelligent automation tools
Drives the maturation of the quantum software stack toward production-grade stability
Improves the throughput of cryogenic measurement facilities via efficient data analysis software
Bridges the talent gap between classical software engineering and experimental quantum physics
Secures the reproducibility of breakthroughs in solid-state quantum computing architectures
Industry Tags: Quantum Computing, Silicon Spin Qubits, Research Software Engineering, Intelligent Automation, Hardware Characterization, Instrument Control, Quantum Value Chain, Software Infrastructure, Data Acquisition Systems, Fault Tolerant Computing
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