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 Munich, Germany. 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 German & English.
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 evolution of commercial deep-tech architectures demands specialized architectural leaders who can translate novel algorithmic paradigms into stable, enterprise-ready infrastructure. Within the advanced software landscape, the emergence of Senior Solution Architects represents a critical bridge between laboratory-scale technical discovery and complex industrial application deployment. As global enterprises seek to integrate specialized artificial intelligence and early-stage quantum solutions, the primary bottleneck has shifted from theoretical algorithmic feasibility to production-grade deployment. Current industry focus lies on bridging classical and quantum capabilities at scale, ensuring long-term architectural interoperability. This specialized function mitigates systemic execution risks by embedding high-compute frameworks within traditional enterprise software ecosystems. Market signals across the European technology landscape confirm that such pre-sales engineering expertise is vital for accelerating the commercial adoption of deep-tech portfolios.
The deep-tech enablement sector is navigating a structural transition as advanced computational models migrate toward hybrid, cloud-native enterprise environments. While the development of fundamental AI frameworks and quantum compilers progresses rapidly, commercial scaling remains constrained by systemic implementation challenges and vendor fragmentation. Organizations frequently struggle to align emerging large language model infrastructures with strict regional data protections and infrastructure architectures. Consequently, the value chain requires authoritative architectural experts who can de-risk integration cycles, establish predictable total-cost-of-ownership models, and manage complex pre-sales technical workflows.
In continental hubs like Germany, this tension is exacerbated by rigorous localized requirements for operational sovereignty, regulatory compliance, and hybrid infrastructure resilience. Enterprise buyers demand deterministic evidence of benchmarked performance, data privacy safeguarding, and infrastructure efficiency before committing capital to deep-tech deployment. This introduces significant friction at the intersection of business value and abstract computer science.
To address these macro limitations, industry dynamics emphasize roles that unify high-performance infrastructure orchestration with applied mathematical frameworks. By providing deep technical advisory during early procurement cycles, these enablement functions insulate buyers from technology obsolescence. This structural alignment is critical for transforming abstract computational advantages into verified, industry-specific market solutions.
The capability matrix for this role type centers on the synchronization of advanced machine learning pipelines with enterprise-grade cloud systems. Structural mastery of deep learning optimization, including quantization frameworks and inference accelerators, is crucial for reducing runtime overhead across legacy hardware tiers. This requires deep orchestration expertise using modern containerization technologies and distributed computing infrastructure across multi-vendor cloud networks. These engineering proficiencies are fundamental to the throughput of technical field operations, enabling the systematic validation of complex workloads prior to full-scale enterprise delivery. By defining robust automation architectures and multi-agent frameworks, this function establishes a reproducible path for complex system deployment. - Accelerates the transition of advanced AI frameworks into deterministic enterprise applications
- Mitigates architectural compliance risks by aligning deep-tech deployments with regional data regulations
- Facilitates stable deployment topologies across hybrid, public, and sovereign cloud environments
- Optimizes infrastructure resource allocation through rigorous benchmarking of high-compute workloads
- Reduces integration friction between abstract algorithmic research and established corporate software stacks
- Enhances commercial procurement clarity by translating technical metrics into measurable business outcomes
- Supports long-term platform scalability through the design of robust MLOps orchestration frameworks
- Streamlines the pre-sales engineering cycle for complex multi-vendor computing architectures
- Minimizes capital risk for enterprise buyers evaluating early-stage quantum and advanced AI systems
- Strengthens technical ecosystem interoperability by utilizing open model formats and execution layers
- Cultivates regional market trust through authoritative technical advisory and transparent solution blueprinting
- Orchestrates cross-functional alignment between engineering teams, external partners, and executive stakeholdersIndustry Tags: Deep Tech Architecture, MLOps Optimization, Hybrid Cloud Systems, Enterprise Software Integration, Inference Infrastructure, Artificial Intelligence Enablement, Sovereign Deployment Models, Pre-Sales Engineering, Computational Scaling
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