Founded in 2020 and based in the heart of Paris, C12’s mission is to be at the center of one of the biggest technological breakthroughs of the century and change the course of history by building a universal quantum computer.
At C12, we believe that achieving a true breakthrough in quantum computing requires rethinking the fundamentals. That’s why our founders—deeply rooted in academic and engineering excellence—have chosen carbon nanotubes as the building blocks of our quantum processors. This ultra-pure material dramatically reduces error rates, boosts performance, and minimizes hardware overhead—key ingredients for scalable, fault-tolerant quantum computing. By crafting a unique approach that scales, we aim to revolutionize quantum computing just as silicon transformed classical computing.
Since our founding, we’ve raised over €25 million in funding, published 11 scientific papers, and secured 8 patents. Today, our fast-growing team of 80+, including 25 PhDs, has over 26 nationalities represented. We have our own cutting-edge lab spaces in Paris' historic Panthéon district, where scientists, engineers, and innovators work side-by-side to tackle some of the most exciting technical challenges of our time.
If you're passionate about shaping the future of quantum technology and want to make a real impact, C12 offers a unique environment to grow, learn, and innovate.
\n
Your role at C12 Quantum Electronics:
We are seeking our first AI Automation Engineer to act as a technical force multiplier across the entire organization. This is a transversal role: you will sit at the intersection of IT/Software/Infrastructure, R&D and Operations to identify operational bottlenecks and solve them with intelligent systems.
You won't just be "automating tasks", you will be architecting stateful AI workflows and deploying autonomous agents that handle complex, multi-step logic using self-hosted LLM infrastructure. You will balance rapid iteration with production-grade engineering to build a truly AI-native quantum computing organization while maintaining complete data sovereignty.
Key responsibilities:
- Transversal Solution Architecture: Partner with teammates across R&D, IT Operations, and Software teams to map their workflows and design end-to-end AI systems that solve their specific operational challenges
- Self-Hosted LLM Infrastructure: Deploy, maintain and optimize local LLM infrastructure (Ollama, vLLM, or similar) to power intelligent automation while ensuring data security and compliance with research confidentiality requirements
- Hybrid Automation & Agentic Systems: Build robust pipelines using workflow orchestration tools, python for custom logic and agentic frameworks powered by self-hosted models for intelligent reasoning
- Full-Stack Prototyping: Own the full lifecycle—from identifying an opportunity to shipping a production-ready internal tool (e.g. automated documentation systems, intelligent task management or infrastructure monitoring agents).
- Extending AI Capabilities: Develop and maintain MCP (Model Context Protocol) servers and API integrations to give our self-hosted agents secure access to internal systems (Google Workspace, Nextcloud, monitoring tools) and quantum computing platforms.
- AI Observability & Iteration: Implement feedback loops to track the performance and reliability of your automations, moving from "vague prompts" to deterministic, high-quality outputs.
- Infrastructure Integration: Work closely with existing infrastructure (OVH cloud, Tailscale VPN, Ansible automation) to deploy secure, scalable AI-powered solutions on our private infrastructure
About you:
- Builder Mindset: You are an "AI-native" engineer who excels at turning ideas into working systems with a portfolio of personal projects or previous experience showing you can build "end-to-end.
- Orchestration: You have experience with Prefect, LangGraph, n8n, or similar workflow engines that can integrate with self-hosted LLM endpoints.
- Languages: You have strong proficiency in Python (for data processing, automation, and agent logic).
- LLM Engineering: You have a deep understanding of RAG, tool-calling, Prompt Engineering and MCPs. Experience adapting these techniques for open-source models and local deployments is a plus
- Infrastructure & DevOps: You have a solid understanding of cloud infrastructure (OVH preferred), containerization (Docker), GPU management, VPN solutions (Tailscale) and configuration management (Ansible).
- Transversal Communication: You can translate a "business pain" into a "technical implementation" and explain your architectural choices to both technical and non-technical peers, including quantum researchers.
- Security-First Approach: You have a strong understanding of secure automation practices, data sovereignty and the importance of keeping sensitive research data on-premises. Experience with private networking and access control is a plus
- Adaptability: You thrive in ambiguity and are excited by the prospect of touching every part of a growing deep-tech quantum computing startup.
\n
What we offer:
- 46,000 - 50,000 euros yearly base salary
- Stock options for every employee (BSPCE/ESOP)
- Sponsored trip to conferences around the world
- A highly dynamic international team
- Swile meal vouchers
- Mental health support with moka.care
- Annual Learning & Development Allowance
- Sabbatical leave (after 2 years in the company)
- Vibrant office culture (two office spaces in the heart of Paris, team lunches, offsite events, Friday breakfasts..)
You should join us if...
- You like hands-on work and technology
- You want to contribute to achieving landmark results in quantum computing, making a difference in the emerging quantum technologies
- You want to work within a team of 80+ people with various backgrounds in nanofabrication, quantum electronics, and carbon nanotube science to create a revolutionary quantum computing processor
- You want to thrive in an exceptional scientific environment with several industrial and academic partners
- You share our values (excellence, scientific integrity, diversity, curiosity, and care) and want to help us define our product-focused culture and ambition to accelerate
We still encourage even if you don’t meet all the requirements. Rest assured, we are committed to finding the right fit for our team and are open to adjusting compensations based on skills and experiences.
Applications from women are especially welcomed!
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of specialized AI Automation Engineers within deep-tech hardware sectors addresses a critical structural bottleneck in scaling complex scientific workflows. As quantum hardware developers expand physical qubit arrays, traditional manual and semi-automated operational paradigms introduce prohibitive friction across cross-functional R&D pipelines. Positioned at the intersection of systems engineering, internal infrastructure, and scientific operations, this role type serves as an operational force multiplier that accelerates technology readiness levels (TRLs). By deploying self-hosted, sovereign artificial intelligence architectures and agentic automation, the function ensures that proprietary research data remains secure while eliminating process fragmentation. Organizations like C12 Quantum Electronics rely on this operational layer to bridge the gap between abstract hardware engineering and continuous computational throughput. Ultimately, this structural enablement optimizes resource allocation and safeguards intellectual property across the broader quantum ecosystem.
The deep-tech innovation lifecycle relies heavily on rapid iteration loops between theoretical design, nanofabrication, and experimental testing. However, as hardware architectures grow in complexity, administrative and infrastructural overhead can severely dampen organizational velocity. Current industry focus lies on bridging classical and quantum capabilities at scale, requiring deep-tech ventures to modernize internal operational environments alongside external product offerings.
A primary macro constraint facing the quantum computing sector is the friction of multi-disciplinary coordination. Hardware R&D involves highly specialized domain experts across physics, materials science, and software development, creating fragmented data silos and non-standardized communication channels. Integrating autonomous workflows and self-hosted intelligence systems standardizes operational knowledge transfer, reducing the cycle time required to translate experimental outcomes into engineering adjustments.
Furthermore, data sovereignty and intellectual property security remain paramount considerations for deep-tech hardware startups. External cloud dependencies for AI automation often introduce unacceptable risks regarding proprietary nanofabrication protocols and chip design specifications. By embedding private, local AI infrastructure within core operations, entities maintain full control over sensitive research outputs while capturing the efficiency gains of modern agentic systems.
The capability architecture for AI Automation Engineering centers on orchestrating local large language model deployment, stateful workflow automation, and private API integrations. Implementing self-hosted LLM execution engines combined with model context protocol adapters enables seamless, secure communication between internal communication tools, developer repositories, and private compute clusters. This architectural coupling ensures that automated agents execute complex multi-step routines with high precision and full observability.
Furthermore, the integration of robust workflow orchestration frameworks and containerized microservices provides the underlying stability required for production-grade internal platforms. Bridging classical cloud management tools with specialized hardware execution systems reduces manual data handling between research teams. This cross-functional linkage directly enhances data provenance, system reliability, and overall organizational throughput across the technology lifecycle. - Accelerates the transition of quantum hardware R\&D from experimental prototyping to scalable manufacturing processes
- Mitigates data security risks by implementing sovereign on-premises artificial intelligence infrastructures for research operations
- Streamlines cross-functional workflows between scientific research, software development, and internal IT operations
- Enhances data integrity across experimental pipelines by establishing deterministic tool-calling and automated documentation frameworks
- Reduces operational friction in deep-tech organizations by deploying stateful agentic systems for multi-step task resolution
- Minimizes software integration latency between classical computing environments and private hardware testing systems
- Strengthens intellectual property protection by eliminating third-party cloud dependencies for confidential R\&D automations
- Optimizes technical workforce allocation by automating repetitive administrative, monitoring, and operational overhead
- Shortens hardware iteration cycles through automated synthesis and distribution of internal research findings
- Facilitates organizational scaling by embedding self-service developer utilities across heterogeneous technical departments
- Stabilizes internal IT ecosystems by standardizing containerized deployment and private network configuration protocols
- Elevates overall operational maturity across emerging deep-tech enterprises through continuous system observability and feedback loopsIndustry Tags: AI Automation, Self-Hosted LLMs, Deep Tech Operations, Data Sovereignty, Agentic Systems, Workflow Orchestration, Quantum Infrastructure, Model Context Protocol, Private Cloud Architecture
Keywords:
NAVIGATIONAL: C12 Quantum Electronics AI careers, C12 Quantum Electronics engineering jobs, AI Automation Engineer Paris positions, C12 Quantum Electronics research team, C12 Quantum Electronics automation opportunities, C12 Quantum Electronics software infrastructure jobs, C12 Quantum Electronics technical force multiplier roles
TRANSACTIONAL: apply for AI Automation Engineer roles, hiring AI automation engineers in Paris, deep tech AI engineer job openings, private LLM infrastructure engineering jobs, agentic workflow engineer career opportunities, apply for quantum electronics software roles, enterprise AI automation job vacancies
INFORMATIONAL: self-hosted LLM architecture in deep tech, agentic AI workflows for hardware research, data sovereignty in quantum computing startups, model context protocol implementation for internal tools, streamlining R\&D workflows with autonomous agents, local LLM deployment for secure research environments, role of AI automation in quantum engineering
COMMERCIAL INVESTIGATION: best practices for self-hosted LLM automation, top agentic workflow engines for deep tech, evaluating private cloud AI infrastructure for R\&D, comparing workflow orchestration tools for scientific computing, best companies for AI infrastructure engineers in Paris, leading private AI frameworks for hardware startups
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.
