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 Paris, France. 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: Fluent in French & English.
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 structural evolution of the deep-tech sector necessitates specialized architectural functions that bridge advanced algorithmic capabilities with industrial enterprise environments. As multi-modal artificial intelligence platforms and quantum computing protocols transition toward commercial viability, organizations encounter severe integration hurdles when deploying high-compute software architectures into legacy cloud frameworks. The emergence of senior solution architecture roles within the application enablement layer serves as a critical translation mechanism, converting abstract software competencies into predictable customer-facing topologies. Market dynamics indicate that mitigating the friction between raw mathematical capabilities and enterprise-grade infrastructure scaling is the primary determinant for securing competitive differentiation. Consequently, this role type provides the essential technical stewardship required to stabilize multi-tenant systems, reduce inference overheads, and navigate evolving compliance mandates across regulated economic zones.
Current industry focus lies on bridging classical and quantum capabilities at scale. Within the deep-tech value chain, software enablement functions act as a primary interface between upstream algorithmic research and downstream commercial application. The deployment of advanced computational models remains constrained by severe hardware and systemic imbalances, particularly regarding the optimal provisioning of graphics processing units and localized tensor processing architectures. As large language models and multi-modal neural networks expand in parameter size, the broader ecosystem faces critical bottlenecks related to operational energy consumption and escalating cloud inference expenditures.
Simultaneously, the global deep-tech landscape must navigate complex geopolitical and regulatory variations, particularly within the European Union where strict data sovereignty policies demand localized or hybrid deployment models. The fragmentation of machine learning engineering tools and the absence of standardized testing benchmarks further complicate the modernization of legacy enterprise resource planning platforms. Organizations are increasingly forced to manage these technical dependencies while contending with a persistent deficit of personnel capable of operating fluidly across separate computational frameworks.
The stabilization of these enterprise environments depends on the strategic execution of pre-sales architecture teams. By establishing deterministic integration pathways for optimized software frameworks, this function addresses macro-level scalability bottlenecks and helps commercial entities navigate the multi-year transition toward fault-tolerant quantum utility.
The capability architecture for this solution deployment function rests upon the synchronization of multi-modal machine learning pipelines with production-grade cloud infrastructure layers. Mastery over the model delivery ecosystem requires comprehensive expertise in inference orchestration stacks, specialized serialization formats, and automated deployment frameworks to maximize computational throughput across public and sovereign hyper-scaler networks. These technical proficiencies are vital for reducing systemic latency, optimizing memory allocation, and managing the high capital expenditures associated with enterprise-scale deep-tech architectures.
Furthermore, this architectural paradigm interfaces directly with advanced engineering domains by orchestrating the containerization of highly complex data processing workloads across distributed hardware topologies. The integration of advanced retrieval-augmented generation pipelines and multi-agent systems ensures that data architectures remain resilient under fluctuating enterprise workloads. This technical configuration provides the foundational stability required to govern complex data life cycles, verify performance metrics, and guarantee alignment with regional ethical frameworks and evolving compliance statutes. - Accelerates the market adoption of high-performance deep-tech software solutions within complex enterprise environments
- Mitigates capital allocation risk by providing rigorous technical validation and benchmarking for large-scale algorithmic deployments
- Optimizes infrastructural efficiency by implementing advanced compression and quantization protocols across cloud networks
- Facilitates the seamless synchronization of multi-modal artificial intelligence pipelines with existing legacy data structures
- Reduces operational friction throughout the pre-sales life cycle by translating scientific breakthroughs into functional commercial architectures
- Minimizes implementation latency through the standardization of containerized workload orchestration frameworks across hybrid cloud environments
- Strengthens organizational risk mitigation strategies by ensuring strict adherence to regional data privacy and compliance mandates
- Enhances global ecosystem interoperability by bridging the technical gap between sovereign cloud architectures and scalable public hyper-scalers
- Lowers enterprise computing expenditures by orchestrating the precise sizing and allocation of specialized hardware infrastructure
- Supports the orderly transition of industry verticals from classical optimization models toward future quantum-assisted workflows
- Promotes technical standardization across the deep-tech value chain by establishing repeatable solution design methodologies
- Secures predictable scaling trajectories for commercial organizations adopting advanced retrieval and multi-agent systemsIndustry Tags: Deep Tech Architecture, Machine Learning Integration, Enterprise Software Enablement, Sovereign Cloud Deployment, Large Language Model Optimization, Hybrid Compute Infrastructure, Pre-Sales Engineering, Computational Scalability
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