Multiverse Computing
Multiverse Computing is a fast-growing deep-tech company founded in 2019 and recognized by CB Insights as one of the 100 most promising AI companies globally. We are the largest quantum software company in the EU, with 250+ employees worldwide building advanced AI and quantum solutions that help enterprises tackle complex, high-impact challenges across industries such as finance, energy, manufacturing, telecom, and industrials.
Our mission is to enable organizations to gain a meaningful competitive edge through cutting-edge AI and quantum technologies.
Why join us?
We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%. Joining us means working on cutting-edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum-AI unicorn in the making.”
This opportunity is to work in our offices in London, UK. We are looking for a talented Solutions Architect to join our pre-sales team and bridge the gap between our technology and our customers’ needs, crafting innovative, scalable, and robust AI- powered solutions.
Key requirements:
- Previous experience in a technical partner pre-sales or consulting role with a heavy emphasis on partner and customer-facing interactions (i.e. Solutions Architect, Sales Engineer, Implementation Consultant)
- Excellent communication and presentation skills, able to interface effectively with technical and non-technical stakeholders; experience writing technical proposals or responding to RFPs/tenders; experience running hands-on product demos independently.
- Strong knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML services (e.g., SageMaker, Vertex AI, AzureML), including sovereign, on-premise, and hybrid deployment models.
- Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks (e.g., Kubeflow, Flyte, MLflow); proficiency with Docker and Kubernetes for AI workload containerization.
- Understanding of LLM inference stacks (vLLM, llama.cpp, OpenVINO) and model delivery formats (ONNX, .safetensors, HuggingFace model hub).
- Experience sizing GPU infrastructure for LLM inference or training workloads (memory, throughput, hardware tiers from A10 to H200).
- Experience benchmarking and evaluating LLM performance (accuracy, latency, throughput).
- Hands-on coding skills in Python, SQL, and familiarity with ML libraries and frameworks (PyTorch, TensorFlow, Hugging Face).
- Bachelor's or master's degree in Computer Science, Data Science, Engineering, or related field.
- Must be available to travel as needed for meetings, conferences, and project requirements.
Preferred Qualifications:
- Experience with Computer Vision models, Speech models, Vision-Language models, and other modalities.
- Experience with AI model optimization, quantization, or deployment to edge devices.
- Hands-on experience designing RAG pipelines and/or multi-agent systems..
- Experience designing data architectures (batch & streaming) and working with big data technologies.
- Knowledge of data privacy and ethical considerations in AI, including GDPR compliance and familiarity with the EU AI Act.
Location: Applicants must have legal authorization to work in the country where the position is based
Perks & Benefits:
- Relocation package (if applicable).
- Eligibility for educational budget according to internal policy.
- Language classes and discounted lunch options
- Working in a high paced environment, working on cutting edge technologies.
- Career plan. Opportunity to learn and teach.
As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The evolution of the deep-tech sector from theoretical exploration to commercial deployment has created a critical structural requirement for Solution Architects capable of bridging complex algorithmic capabilities and enterprise-grade cloud environments. As organizations navigate the transition across varying Technology Readiness Levels (TRLs), this role type serves as a primary stabilization point within the application enablement layer, ensuring that emerging computing solutions are architecturally compatible with classical enterprise infrastructures. Market signals from global technology strategy reports highlight that cross-functional coordination is essential to mitigate the systemic risks of technology fragmentation in high-compute industries. By translating advanced technical frameworks into deterministic enterprise architecture, this function secures the foundation for long-term commercial readiness and competitive differentiation within the global deep-tech value chain. Current industry focus lies on bridging classical and quantum capabilities at scale to optimize enterprise resource planning and high-performance workflows.
The commercial application enablement layer of the deep-tech ecosystem is undergoing a decisive shift from laboratory-scale proof-of-concepts to the integration of high-fidelity computational kernels within global enterprise environments. While basic research continues to progress, the primary bottleneck for industrial adoption has shifted to the architectural layer, specifically regarding the reproducibility, orchestration, and scalability of complex deployment models. Enterprise translation buyers face an increasingly complex operational reality, necessitating sophisticated management of the software-hardware interface to ensure that hybrid workflows can seamlessly handle the data throughput requirements of production environments.
Workforce scarcity is particularly acute at the intersection of domain-specific industrial variables, classical cloud infrastructure, and advanced software layers. As enterprise organizations move beyond early-stage benchmarks, the wider tech ecosystem requires specialized architects who can navigate vendor fragmentation, sovereign data policies, and a lack of standardized orchestration protocols. Current industry dynamics place a premium on functional layers that can drive interoperability across disparate cloud platforms, acting as the primary mechanism for maintaining momentum across the deep-tech value chain.
Integration with existing high-performance computing (HPC) environments remains a high-stakes dependency for major commercial sectors. The evolution of the enterprise software ecosystem depends on the ability to translate advanced data structures, machine learning microservices, and specialized optimization problems into native formulations without disrupting established corporate IT frameworks. Consequently, the availability of senior technical architects capable of orchestrating these complex cross-functional dependencies is a primary determinant of whether a commercial organization can successfully transition from exploration to active deployment.
The capability architecture for this role type centers on the synchronization of advanced artificial intelligence frameworks, large language model inference stacks, and enterprise systems engineering protocols. Mastery of the hardware-agnostic orchestration layer is essential for ensuring that complex computational models are optimized for the specific constraints of cloud-native infrastructure, including memory bandwidth, latency thresholds, and hardware tier efficiencies. This requires a deep understanding of the integration points between high-level application programming interfaces (APIs) and the underlying deployment frameworks that manage hybrid classical workflows. These capabilities are fundamental to the throughput of deep-tech organizations, as they enable the parallelization of pre-sales research initiatives alongside the development of scalable corporate architectures. By establishing rigorous verification and performance evaluation frameworks, this function provides the leverage needed to assess true business value before full-scale capital allocation. Furthermore, the ability to manage complex stakeholder landscapes ensures that scientific outputs are reconciled with the practical constraints of regulatory compliance, data sovereignty, and upcoming legislative requirements like the EU AI Act, thereby reducing iteration friction between research and delivery. - Accelerates the deterministic transition from complex software models to industrial-grade enterprise applications
- Mitigates systemic execution risks by synchronizing advanced technical capabilities with near-term technology roadmaps
- Facilitates the integration of advanced computational kernels into standardized cloud and high-performance computing infrastructures
- Strengthens the reliability of organizational technology strategies through the implementation of rigorous framework benchmarking
- Reduces iteration friction between technical breakthroughs and the deployment of scalable software architectures
- Optimizes the allocation of specialized technical talent across pre-sales development and strategic corporate portfolios
- Enhances the stability of the deep-tech value chain by providing predictable requirement frameworks for external partners
- Supports the scaling of application capabilities by managing the complex dependencies of hybrid classical workloads
- Improves the transparency of technology readiness level progression for stakeholders in the commercial and investment sectors
- Enables the structural reproducibility of advanced computing deployments through the standardization of implementation protocols
- Protects high-capital research and development investments by ensuring alignment between technical discovery and commercial scalability
- Orchestrates the convergence of complex analytical software pathways with the practical demands of global enterprise servicesIndustry Tags: Enterprise Cloud Architecture, Pre-Sales Engineering, Machine Learning Orchestration, Hybrid Computing Systems, Model Inference Stacks, Tech Sovereignty, Large Language Model Deployment, Technical Consulting, Infrastructure Scaling
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