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 Milan, Italy. 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.
- Languages: French & English (mandatory), Spanish (preferred).
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.
- Languages: Fluent in French & English (Mandatory) & Spanish (preferred). No Italian proficiency required.
Perks & Benefits:
- Sales Commission structure.
- 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.
- Progressive Company. Happy people culture
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 emergence of Senior Solution Architects specializing in deep-tech and pre-sales engineering signifies a critical shift in the quantum and artificial intelligence value chain from isolated algorithmic research to commercial application enablement. As the technology landscape transitions through varying technology readiness levels, the structural necessity for roles that bridge highly specialized, abstract software solutions and established enterprise infrastructures becomes paramount to resolving industrial adoption barriers. This role type serves as a primary mechanism for reducing integration friction within the application enablement layer, ensuring that sophisticated algorithmic models are architecturally compatible with existing cloud environments. Verifiable market signals from national technology strategies and global technology consortia indicate that such cross-disciplinary expertise is essential to mitigate the systematic execution risks of corporate technology investments. By translating intricate deep-tech developments into scalable, robust client frameworks, this function secures the foundation for enterprise readiness and competitive differentiation across high-compute industries.
The commercialization of advanced computational software is undergoing a decisive shift from laboratory-scale proof-of-concepts to the deployment of production-grade solutions within global enterprise ecosystems. While deep-tech development continues to progress across diverse software and hardware modalities, the primary bottleneck for industrial adoption has shifted to the implementation layer, specifically regarding the integration of advanced optimization tools into classical IT workflows. The current sector-wide focus lies on bridging classical and quantum capabilities at scale, necessitating a sophisticated management of the software-hardware interface to ensure that hybrid workflows can handle the data throughput and inference constraints of production environments.
Workforce scarcity is particularly acute at the intersection of domain-specific industrial variables and advanced solution architecture. As global organizations move beyond initial benchmarking phases, the broader deep-tech ecosystem requires specialized consultants who can navigate the fragmentation of emerging software stacks and the lack of standardized deployment protocols. Current industry dynamics place a premium on roles that can drive interoperability across disparate sovereign, on-premise, and hybrid cloud platforms, mitigating the risks of vendor lock-in for enterprise clients. This structural layer of expertise acts as the primary conduit for maintaining market momentum, resolving the systemic "translation gap" between theoretical performance and commercial utility.
Integration with existing cloud infrastructures remains a critical dependency for the deep-tech sector. The evolution of the commercial value chain depends on the ability to translate complex financial optimization, logistics, and machine learning problems into native algorithmic formulations without disrupting established enterprise architectures. Consequently, the availability of technical pre-sales architects capable of orchestrating these cross-functional dependencies determines whether an enterprise can successfully transition from exploration to active deployment.
The capability architecture for this role type centers on the synchronization of advanced artificial intelligence and quantum computing frameworks with the strict protocols of enterprise-grade systems engineering. Mastery of model delivery formats and containerization tools is essential for ensuring that deep-tech applications are fully optimized for the specific constraints of distributed cloud architectures, data pipelines, and high-performance computing clusters. This requires a comprehensive understanding of the integration points between high-level application programming interfaces and underlying hardware infrastructure tiers, managing performance metrics such as throughput, latency, and resource efficiency.
These capabilities are fundamental to the operational throughput of deep-tech organizations, as they enable the parallelization of pre-sales research initiatives alongside the deployment of scalable production architectures. By establishing rigorous verification, validation, and benchmarking frameworks, this function provides the necessary leverage to assess the economic viability of advanced software before full-scale corporate capital allocation. Furthermore, the ability to manage complex stakeholder landscapes ensures that deep-tech outputs are reconciled with the practical constraints of regulatory compliance, data privacy, and international data sovereignty mandates. Such structural expertise reduces iteration friction between abstract software engineering and commercial client delivery, which remains critical for long-term interoperability within the emerging technology-as-a-service market. - Accelerates the deterministic transition from theoretical deep-tech software research to industrial-grade enterprise applications
- Mitigates systemic execution risks by aligning corporate infrastructure constraints with advanced algorithmic deployment requirements
- Facilitates the seamless integration of high-performance computational kernels into standardized cloud and on-premise networks
- Strengthens the reliability of organizational technology strategies through the implementation of rigorous performance benchmarking
- Reduces iteration friction between fundamental software optimization breakthroughs and the deployment of scalable enterprise solutions
- Optimizes the alignment of specialized technical pre-sales talent across research, development, and commercial consulting portfolios
- Enhances the stability of the deep-tech value chain by providing predictable requirement frameworks for enterprise clients
- Supports the scaling of advanced machine learning models by managing the complex dependencies of hybrid workflows
- Improves the transparency of technology readiness level progression for stakeholders in the industrial and investment sectors
- Enables the structural reproducibility of algorithmic implementations through the standardization of architectural deployment protocols
- Protects high-capital corporate technology investments by ensuring alignment between scientific innovation and commercial scalability
- Orchestrates the convergence of advanced deep-tech software pathways with the practical operational demands of global enterprisesIndustry Tags: Solution Architecture, Pre-Sales Engineering, Deep Tech Integration, Hybrid Cloud Infrastructure, Algorithmic Benchmarking, Enterprise Software Enablement, Model Optimization, Distributed Systems, Technology Translation
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