About The Role and Team
Quantum Motion is a fast-growing quantum computing scale-up based in London founded by
internationally renowned researchers from UCL and Oxford University with over 40 years’
experience in developing qubits and quantum computing architectures. Bringing together state-of-
the-art cryogenic facilities and an outstanding interdisciplinary team, we are developing quantum
processors based on industrial-grade silicon chips, with the potential to radically transform
computing power in areas such as materials modelling, medicine, artificial intelligence and more.
Our Team
Since 2021 our team has been listed every year in the “Top 100 Startups worth watching” in the EE Times in 2021 and 2022, 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, and we have recently closed our Series C funding of $160 million.
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.
This is a rare and exciting opportunity to be an employee at a scale-up shaping the future of quantum computing. There are vast opportunities for professional growth and to make an impact within the company.
Our team of 100+ is based across London, Oxford, San Sebastián and Sydney, with our primary hub in Islington (London).
Functions of the Role
The Cloud Engineering Team builds cloud-based solutions, supports the cloud platform and helps to drive its adoption and expansion while also collaborating with other teams. Furthermore, the Cloud Engineering Team looks after the on-prem IT infrastructure.
The primary function of this position will be to take responsibility for the successful development, management and maintenance of software services to ensure that the production services perform effectively, while maintaining a high level of internal customer satisfaction. This includes developing, maintaining, supporting, and optimising key functional areas, particularly internal applications and servers in a hybrid infrastructure. You will develop, troubleshoot and resolve software and process problems in a timely and effective fashion.
This is a rare and exciting opportunity to be an early employee at a start-up shaping the future of quantum computing, and in particular to form a new team within the company. There are vast opportunities for professional growth and to make an impact within the company.
As a Senior DevOps Engineer, you will bridge the gap between high-level strategy and hands-on execution. This role requires a robust software engineering background, a diagnostic problem-solving mindset, and a relentless passion for automation. Given our hybrid footprint, this position offers the unique challenge of managing both cloud-native services and physical on-site infrastructure.
Functions of the Role
- Design, provision, and manage large-scale, highly available cloud infrastructure platforms, optimizing for heavy compute workloads and high-concurrency applications.
- Implement and maintain modular IaC (Terraform or CloudFormation) to automate the provisioning of secure, low-latency, and multi-region cloud environments.
- Architect and maintain high-performance, high-IOPS cloud storage solutions, ensuring seamless data synchronisation, backup strategies, and optimal throughput.
- Automate the deployment, configuration, and auto-scaling of workload orchestrators, container platforms, and core service components to ensure high resource utilisation and cost efficiency.
- Enforce strict security protocols across all environments by implementing Zero Trust architectures, secure IAM roles, and automated configuration management (e.g., Ansible, Packer) for hardened OS images.
- Provide expert-level support for complex infrastructure and platform issues, documenting automated workflows and mentoring junior team members on cloud-native best practices.
Experience - Essentials
- Proven track record of architecting, deploying, and maintaining production-grade, highly scalable workloads on AWS.
- Deep expertise in Terraform (preferred) and CloudFormation for building modular, version-controlled infrastructure environments.
- Solid knowledge of high-performance and distributed cloud storage solutions (e.g., Amazon FSx, EFS, EBS, NetApp ONTAP).
- Expert-level proficiency with Enterprise Linux distributions (RHEL/Rocky/Ubuntu) and systems-level troubleshooting.
- Professional proficiency in Python or Bash for building automation tooling, custom monitoring scripts, and complex system integrations.
- Deep understanding of enterprise IAM patterns, including Active Directory/LDAP integration for consistent access control across hybrid environments.
- Ability to translate complex infrastructural scaling decisions into actionable guidance for development teams and cost-benefit updates for leadership.
Experience - Desirable
- Proven track record of architecting and deploying production HPC workloads on AWS using AWS ParallelCluster, SOCA or custom Slurm fleets.
- Ability to architect for maximum cost efficiency, implementing automated spot-instance utilization, auto-scaling strategies, and Savings Plans optimization.
- Proficiency in monitoring infrastructure health and application performance using tools like Prometheus, Grafana, ELK stack, or CloudWatch, with a focus on identifying bottlenecks.
- Experience designing and implementing automated CI/CD pipelines (e.g., GitLab CI, GitHub Actions, Jenkins) for software and infrastructure deployment.
- Certifications: AWS Certified Solutions Architect Professional, AWS Certified DevOps Engineer Professional, or AWS Certified Advanced Networking.
Benefits
● Be part of a creative, world-leading team
● Competitive salary and share options scheme
● Contributory pension scheme
● Private Medical Insurance
● 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 or any other protected characteristics covered by the Equality Act 2010.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The integration of systems engineering principles within deep-tech hardware scaling represents a fundamental requirement for the transition of quantum processors from pilot systems to reproducible, industrial-grade compute clusters. As organizations advance solid-state and silicon-based qubit designs, the primary operational bottleneck shifts from isolated physical manipulation to the configuration of stable, hybrid high-performance computing environments. Senior systems and deployment specialists bridge this translation gap by establishing resilient infrastructure backbones that unify highly specialized on-premise hardware control assemblies with distributed cloud-native resources. This structural optimization directly influences value-chain velocity, ensuring that hardware engineering cycles are decoupled from platform execution failures. By implementing automated deployment frameworks and robust identity structures, this role type safeguards systemic integrity and enables concurrent, multi-region algorithmic experimentation across the emerging deep-tech ecosystem.
The quantum hardware and software landscape is currently undergoing a decisive transition toward hybrid computing continuum models, where classical accelerators work in tight orchestration with early-stage quantum processing units. This structural shift moves beyond laboratory proof-of-concepts, demanding extreme reliability in the underlying deployment layers to accommodate heavy compute workloads and high-concurrency applications. Within this value chain, infrastructure engineering operates at the intersection of physical system control, electronic design automation, and scalable cloud orchestration, functioning as a primary mechanism for scaling technical throughput.
Macro constraints within this deep-tech sector are driven heavily by infrastructure complexity and the severe shortage of engineering talent capable of operating across both classical enterprise patterns and quantum-native parameters. Vendor fragmentation and a lack of unified industry standards for quantum software deployment inject significant technical risks into long-term infrastructure architecture decisions. Consequently, sector-wide efforts continue to address talent and integration challenges in quantum systems to maintain development momentum across varying technology readiness levels.
Furthermore, dependencies on stable public-private funding frameworks and evolving national technology strategies place a high premium on roles that can optimize capital expenditure. Ensuring predictable resource utilization through efficient cloud allocation and robust hardware-software co-design frameworks reduces operational overhead. As global organizations scale their physical footprints across international hubs, establishing secure, multi-region operational infrastructure becomes a critical determinant of competitive survival within the global technology value chain.
The capability architecture for this engineering function requires a sophisticated fusion of infrastructure automation layers, enterprise networking topologies, and highly secure access management paradigms. Expertise in modular infrastructure-as-code deployments represents a baseline necessity, allowing deep-tech organizations to provision deterministic, low-latency execution environments that can safely handle the erratic scaling requirements of advanced research algorithms. This orchestration layer is coupled with high-performance distributed storage subsystems engineered specifically to satisfy the intense input-output throughput demands of hardware co-simulation tools.
These technical capabilities provide essential structural leverage, drastically accelerating internal development loops by establishing continuous integration and automated testing pipelines for hardware-agnostic software stacks. By embedding zero-trust identity architectures across on-prem physical control computers and public cloud environments, this framework guarantees total data integrity while mitigating modern security threats. This rigorous data governance reduces the friction of cross-functional coupling between solid-state physicists, microelectronics engineers, and software architects, enabling the seamless pipeline execution necessary for reliable system benchmarking. - Accelerates the deterministic deployment of hybrid classical-quantum software frameworks across multi-region infrastructure platforms
- Mitigates operational downtime by implementing resilient infrastructure-as-code automation within deep-tech hardware scaling pipelines
- Optimizes capital expenditure through strategic resource allocation and automated workload auto-scaling across high-performance environments
- Enhances data security by architecting comprehensive zero-trust frameworks across on-premise systems and cloud platforms
- Shorter iteration cycles for algorithmic testing via the establishment of standardized continuous integration pipelines
- Reduces integration friction between physics hardware control interfaces and scalable enterprise software services
- Strengthens organizational reproducibility benchmarks by removing manual environmental variance from high-compute cluster deployments
- Facilitates secure cross-border collaboration across international research hubs through unified access management structures
- Minimizes systemic technical risk associated with vendor fragmentation by deploying modular tool-agnostic system platforms
- Supports high-throughput co-simulation workflows by maintaining optimized high-performance distributed storage fabrics
- Stabilizes technology readiness level progression by converting complex experimental infrastructure setups into predictable configurations
- Protects intellectual property assets across hybrid cloud architectures using advanced identity patterns and data governanceIndustry Tags: Systems Engineering, Infrastructure Automation, Hybrid Quantum Computing, Cloud Architecture, Distributed Storage, Zero Trust Security, High Performance Computing, Deep Tech Infrastructure
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