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.
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.
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!
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The integration of specialized AI automation functions within deep-tech hardware and quantum computing organizations represents an operational shift toward software-driven research acceleration and operational optimization. As quantum hardware manufacturers like C12 Quantum Electronics progress from fundamental physics prototyping to scalable, fault-tolerant processing architectures, internal operational friction becomes a critical constraint on organizational velocity. AI automation roles serve as structural force multipliers that reconcile complex cross-functional workflows across physics, fabrication, infrastructure, and software engineering. By deploying self-hosted artificial intelligence models and autonomous orchestration layers, these functions secure proprietary research IP while establishing deterministic execution pipelines across disparate operational units. Consequently, the role directly impacts organizational throughput and resource utilization during key Technology Readiness Level transitions.
The global quantum computing value chain is experiencing a transition where operational efficiency and software maturity are as pivotal as core physical qubit advancements. Deep-tech organizations face unique operational bottlenecks stemming from fragmented software stacks, highly specialized multidisciplinary workflows, and strict requirements for data sovereignty. In hardware-centric startups and scale-ups, cross-functional communication between experimental physics, nanofabrication, and cloud deployment can suffer from translation friction, slowing R&D iteration cycles.
To address these constraints, the deep-tech sector is increasingly adopting self-hosted agentic frameworks and local large language model deployments. This architectural strategy allows organizations to automate multi-step engineering logic, monitoring systems, and documentation without exposing sensitive, IP-heavy research data to external cloud APIs. Current industry dynamics emphasize privacy-preserving automation systems that bridge on-premises computing clusters with internal developer tools.
Furthermore, workforce development within quantum computing relies on maximizing the impact of domain experts in nanofabrication and quantum mechanics. By offloading routine administrative, operational, and software integration tasks to automated pipelines, engineering throughput is preserved for high-value research. Ongoing ecosystem initiatives aim to accelerate readiness for practical quantum applications by establishing software-driven operational baselines that scale alongside physical hardware capacity.
The technical architecture for AI automation roles in deep tech centers on privacy-first local infrastructure orchestration and full-stack workflow integration. Core competency domains include local LLM deployment and inference optimization utilizing containerized frameworks, private networking architectures, and secure state management. Engineers in this space design modular Model Context Protocol interfaces that securely connect autonomous software agents to internal repositories, version control tools, and monitoring suites. These capabilities prevent data leakage while enabling complex contextual reasoning across proprietary codebases. On the orchestration layer, the capability stack requires mastery of workflow execution engines and custom API integrations to transform ambiguous engineering requests into deterministic, traceable system outputs. This end-to-end framework bridges isolated IT infrastructure with active quantum R&D pipelines, ensuring system observability, high operational reliability, and scalable internal tool adoption. - Accelerates internal technology transfer between experimental physics teams and software engineering units
- Secures proprietary quantum R\&D intellectual property through local, self-hosted machine learning execution environments
- Optimizes capital expenditure by streamlining operational workflows across cross-functional deep-tech business units
- Enhances organizational research throughput through the deployment of autonomous workflow orchestration pipelines
- Mitigates software integration friction across hybrid cloud infrastructure, local servers, and specialized lab interfaces
- Improves system observability and operational tracking across internal documentation and project management stacks
- Standardizes data governance protocols for automated agents operating within highly confidential research domains
- Reduces operational overhead for senior research personnel by delegating multi-step administrative logic to intelligent systems
- Establishes scalable software foundations that grow alongside physical quantum processor manufacturing capacity
- Strengthens organizational readiness for rapid scaling by converting manual processes into deterministic software systems
- Minimizes reliance on external API dependencies, protecting supply chain integrity for sensitive computational infrastructure
- Facilitates smoother technology readiness level progression through systematic automation of testing and monitoring tasksIndustry Tags: AI Automation, Self-Hosted LLM, Agentic Workflows, Quantum Electronics, Data Sovereignty, Deep Tech Operations, Model Context Protocol, Infrastructure Orchestration, Private Cloud Infrastructure
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