Multiverse Computing
Multiverse is a well-funded, fast-growing deep-tech company founded in 2019. We are the largest quantum software company in the EU and have been recognized by CB Insights (2023 and 2025) as one of the 100 most promising AI companies in the world.
With 180+ employees and growing, our team is fully multicultural and international. We deliver hyper-efficient software for companies seeking a competitive edge through quantum computing and artificial intelligence.
Our flagship products, CompactifAI and Singularity, address critical needs across various industries:
- CompactifAI is a groundbreaking compression tool for foundational AI models based on Tensor Networks. It enables the compression of large AI systems--such as language models--to make them significantly more efficient and portable.
- Singularity is a quantum- and quantum-inspired optimization platform used by blue-chip companies to solve complex problems in finance, energy, manufacturing, and beyond. It integrates seamlessly with existing systems and delivers immediate performance gains on classical and quantum hardware.
You will be working alongside world-leading experts to develop solutions that tackle real-world challenges. We are looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.
We are committed to building a truly inclusive culture - come and join us.
Job Overview
We are seeking a skilled and experienced Manager with a strong technical background in data science or AI to join our team. In this role you will have the opportunity to leverage cutting-edge quantum and AI technologies to lead the design, implementation, and deployment in production environments of machine learning, vision, or Generative AI systems, as well as working closely with cross-functional teams to integrate these models into our products. You will have the opportunity to work on challenging projects, contribute to cutting-edge research, and shape the future of AI technologies.
As a Manager, you will
- Lead an international team of engineers to develop and deliver cutting-edge solutions to the hardest industry problems for our top-tier clients.
- Lead the technical direction of complex projects, designing a technical solution to the problem and ensuring the quality of the approach and technology used.
- Manage technical projects from initiation to completion, defining the project scope, objectives, and deliverables. To achieve this, you will:
- Develop comprehensive project plans, including timelines, resource allocation, and risk assessments.
- Identify and manage project risks, developing contingency plans to address unforeseen obstacles and maintain project momentum.
- Lead and motivate a diverse team of professionals, providing guidance and direction to ensure project goals are met efficiently and effectively, fostering a collaborative and results-driven team environment.
- Monitor project performance and take proactive measures to ensure the delivery of high-quality solutions.
- Develop documentation, monitor and report project status, assess the effectiveness and accuracy of documentation.
- Foster clear and transparent communication channels with stakeholders, team members, and senior management to provide updates on project status, milestones, and potential challenges.
- Collaborate and communicate with other project managers and leaders to coordinate cross-project initiatives and activities.
- Demonstrate an understanding of customer's tactical goals and effectively participates in the development and implementation of business solutions.
- Contribute to the tech strategy providing clients and technology insights
Required Minimum Qualifications
- Master's or Ph.D. in Artificial Intelligence, Computer Science, Data Science, Physics, Engineering, or related fields, with 5+ years of relevant industry experience.
- 2+ years of successful project management experience in leading and managing engineering teams in a technology-driven company.
- 3+ years of hands-on experience with data science, Generative AI, deep learning (e.g., computer vision, LLMs, etc.), or software engineering technologies in an industrial, non-academic environment.
- Proven experience in the design and implementation of ML or AI systems, including RAG and Agentic AI solutions, and deploying them in cloud-based production environments.
- Proven experience with LLM models, including fine-tuning and alignment techniques to specialize them on different tasks, or pruning and other model compression techniques.
- Excellent command of Python, Docker and Git.
- Experience with GitHub/Gitlab workflows and functionalities.
- Solid understanding of agile development methodologies and best practices.
- Excellent leadership skills with the ability to motivate and guide cross-functional teams effectively.
- Exceptional communication and interpersonal abilities to foster collaboration and maintain strong client relationships.
- Solid organizational and time-management skills, coupled with a keen eye for detail.
- Perfect command of Spanish and English.
Preferred Qualifications
- Ph.D. in Artificial Intelligence, Computer Science, Physics, Engineering, or related fields, with 5+ years of relevant industry experience.
- Experience with deep learning model compression techniques and inference optimization engines (TensorRT, vLLM, etc.).
- Experience in the evaluation of Generative AI systems on real live data.
Perks & Benefits
- Variable performance bonus.
- We offer work visa sponsorship (if applicable).
- Relocation package (if applicable).
- Private health insurance.
- Eligibility for educational budget according to internal policy.
- Language classes and discounted lunch options.
- A high-performance, collaborative environment, operating at pace 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 emergence of Engineering Managers specializing in AI and Machine Learning within the quantum ecosystem represents a critical stabilization point for the transition from experimental software to production-grade deep-tech services. As the sector moves toward hybrid classical-quantum architectures, the structural necessity for leaders who can bridge high-level algorithmic research with industrial engineering rigor becomes paramount to resolving the "readiness gap" between theoretical models and commercial utility. This role type serves as a primary mechanism for scaling the application layer, ensuring that complex innovations in model compression and optimization are architecturally compatible with global enterprise infrastructures. Market signals from major technology analysts highlight that such cross-disciplinary leadership is essential for mitigating the systemic risks of technology obsolescence in high-compute industries. By converting scientific breakthroughs into deterministic technology roadmaps, this function secures the foundation for long-term organizational competitiveness within the emerging quantum-as-a-service market.
The quantum software landscape is undergoing a decisive shift from laboratory-scale proof-of-concepts to the integration of high-fidelity computational kernels within global enterprise ecosystems. While hardware development continues to progress across diverse modalities, the primary bottleneck for industrial adoption has shifted to the systems layer, specifically regarding the reproducibility, scalability, and efficiency of hybrid workflows. The current sector-wide focus lies on bridging classical artificial intelligence and quantum capabilities at scale, necessitating a sophisticated management of the software-hardware interface to ensure that distributed architectures can handle the data throughput requirements of production environments.
Workforce scarcity is particularly acute at the intersection of domain-specific engineering management and advanced machine learning science. As organizations move beyond NISQ-era benchmarks, the ecosystem requires specialized leaders who can navigate the fragmentation of the software stack and the lack of standardized deployment protocols for emerging AI technologies. Current industry dynamics, influenced by international technology strategies and private-capital cycles, place a premium on roles that can drive interoperability across disparate cloud platforms. This structural layer of expertise is the primary mechanism for maintaining momentum as deep-tech solutions transition through varying Technology Readiness Levels.
Integration with existing high-performance computing (HPC) environments remains a high-risk dependency for the sector. The evolution of the value chain depends on the ability to translate complex optimization and generative problems into production-ready formulations without disrupting established enterprise resource planning systems. Consequently, the availability of engineering managers capable of orchestrating these complex cross-functional dependencies is a primary determinant of whether a commercial organization can successfully transition from exploration to deployment. Multiverse Computing and similar high-growth entities are increasingly centralizing this function to maintain leadership in the European quantum software market.
The capability architecture for this role type centers on the synchronization of advanced machine learning research with the protocols of enterprise-grade systems engineering. Mastery of the hardware-agnostic software layer is essential for ensuring that model architectures, such as those based on tensor networks or generative agents, are optimized for the specific constraints of current computational infrastructures. This requires a deep understanding of the integration points between high-level application programming interfaces and the underlying orchestration layers that manage hybrid executions. These capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of research initiatives alongside the development of scalable cloud architectures. By establishing rigorous verification and validation frameworks, this function provides the leverage needed to assess the true business value of AI-enhanced quantum advantage 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 and data sovereignty. Such expertise reduces the iteration friction between abstract research and product delivery, which is critical for long-term interoperability within the global deep-tech value chain. - Accelerates the deterministic transition from theoretical machine learning research to industrial-grade enterprise applications
- Mitigates systemic execution risks by synchronizing long-term algorithmic cycles with near-term technology roadmaps
- Facilitates the integration of quantum-inspired computational kernels into standardized cloud and high-performance computing infrastructures
- Strengthens the reliability of organizational technology strategies through the implementation of rigorous software benchmarking
- Reduces iteration friction between fundamental AI breakthroughs and the deployment of scalable production architectures
- Optimizes the allocation of specialized technical talent across research, development, and strategic liaison portfolios
- Enhances the stability of the deep-tech software value chain by providing predictable requirement frameworks for external partners
- Supports the scaling of optimization capabilities by managing the complex dependencies of hybrid quantum-classical workflows
- Improves the transparency of technology readiness level progression for stakeholders in the investment and policy sectors
- Enables the structural reproducibility of complex AI experiments through the standardization of architectural implementation protocols
- Protects high-capital research and development investments by ensuring alignment between scientific discovery and commercial scalability
- Orchestrates the convergence of academic research pathways with the practical demands of global enterprise-ready servicesIndustry Tags: Quantum Software, Machine Learning Engineering, Model Compression, Hybrid HPC-Quantum, Deep Tech Management, Tensor Networks, Enterprise AI Integration, Systems Architecture, Technology Readiness Levels
Keywords:
NAVIGATIONAL: Multiverse Computing engineering management careers, Multiverse Computing AI leadership positions, Multiverse Computing technical manager jobs, Multiverse Computing quantum software team, Multiverse Computing product development careers, Multiverse Computing San Sebastian office jobs, Multiverse Computing deep tech recruitment
TRANSACTIONAL: apply for engineering manager AI roles, hiring machine learning managers in Europe, quantum software engineering management vacancies, apply for deep tech leadership positions, machine learning production deployment jobs, senior AI engineering manager opportunities, technical project management roles in quantum
INFORMATIONAL: role of engineering management in deep tech, bridging AI research and industrial production, quantum-inspired optimization for enterprise, impact of model compression on AI scalability, managing international engineering teams in quantum, transition from research to production AI, hybrid quantum-classical software architecture explained
COMMERCIAL INVESTIGATION: best companies for quantum machine learning careers, comparing enterprise quantum software providers, top deep tech firms for AI engineering, career paths for machine learning managers, evaluating quantum software impact on industry, leading providers of quantum-inspired AI solutions
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.
