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 prototypes to industrially scalable quantum processors necessitates a fundamental shift in how hardware is characterized and controlled. This Research Software Engineer role type serves as the critical bridge between physical hardware engineering and automated system operation, addressing a structural bottleneck in the quantum value chain. By developing high-performance data acquisition and automation frameworks, these professionals enable the high-throughput testing required to achieve quantum advantage. Market signals indicate that as silicon-based architectures move toward higher technology readiness levels, the ability to automate the characterization of millions of qubits becomes a primary determinant of commercial success. Consequently, this function directly influences the stability and reproducibility of the underlying hardware stack, providing the software infrastructure necessary for fault-tolerant quantum computing to scale beyond current NISQ-era constraints.
The global quantum ecosystem is currently navigating a pivotal shift from fundamental discovery to systems engineering. Within the hardware sector, silicon-based spin qubits have emerged as a high-potential modality due to their compatibility with existing CMOS manufacturing processes. However, a major macro constraint remains the "characterization gap"—the time-intensive process of calibrating and verifying qubit performance at scale. This creates a critical demand for specialized software engineering that can interface directly with cryogenic test environments and measurement instrumentation to automate these workflows.
Industry dynamics reveal that while algorithm development often captures public attention, the maturity of the underlying tooling layer is what dictates development velocity. National quantum strategies, such as those in the UK and EU, increasingly prioritize "quantum-ready" infrastructure that integrates classical software best practices with specialized hardware requirements. This integration is essential for managing the sheer volume of data generated during the development of integrated circuits (ICs) for quantum control. Without robust, automated software stacks, the iteration cycles for hardware design remain prohibitively slow, hindering the path to fault tolerance.
Furthermore, the sector faces a talent mismatch where pure software engineers lack the systems-level context of experimental physics, while researchers often lack the software maturity required for production-grade code. Role types that bridge this divide by applying modern software design principles to experimental automation are essential for ecosystem throughput. As public and private funding cycles move toward rewarding modularity and interoperability, the development of in-house software repositories that standardize measurement protocols becomes a high-leverage activity for hardware developers seeking to maintain a competitive advantage in the silicon quantum race.
Capability domains for this role type center on the intersection of high-performance data acquisition, instrument control, and automated characterization frameworks. Proficiency in building modular software architectures is essential for ensuring that hardware-interfacing code remains extensible as qubit topologies evolve. These capabilities enable the seamless extraction of physical parameters from complex experimental setups, which is the primary mechanism for closing the feedback loop between integrated circuit design and physical qubit performance. Mastery of object-oriented design and numerical processing libraries allows for the translation of raw measurement data into actionable insights for hardware engineering teams. Furthermore, by implementing rigorous software validation and testing protocols, these professionals ensure the stability of the experiment control layer, which is a prerequisite for long-term system reliability. This technical architecture facilitates the structural shift from manual, researcher-led testing to high-velocity, automated hardware verification pipelines, significantly reducing the engineering friction inherent in silicon-based quantum development.
Accelerates the transition from manual qubit calibration to high-throughput automated characterization systems
Reduces the engineering overhead associated with complex hardware-software interface development
Enhances the reproducibility of experimental results through standardized data acquisition frameworks
Strengthens the reliability of the quantum hardware stack via rigorous software testing protocols
Shortens hardware iteration cycles by optimizing the feedback loop between measurement and design
Drives the industrialization of silicon-based quantum processors through scalable automation tooling
Mitigates the risk of technical debt by applying modern software design to experimental codebases
Facilitates the integration of classical control electronics with cryogenic quantum hardware modules
Supports the standardization of measurement protocols across global research and development sites
Improves the accessibility of quantum hardware for non-physics experts through abstract automation layers
Optimizes the utilization of expensive cryogenic and test-and-measurement infrastructure
Advances the commercial readiness of fault-tolerant architectures by stabilizing the software control layer
Industry Tags: Quantum Computing, Silicon Spin Qubits, Experimental Automation, Data Acquisition Systems, Research Software Engineering, Test and Measurement, Integrated Circuits, Hardware-Software Integration, Scalable Quantum Systems, Python Engineering
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