> Markdown version of [/jobs/ext/2726682-cto-and-co-founder](https://www.wearedevelopers.com/jobs/ext/2726682-cto-and-co-founder). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # CTO and Co-Founder - **Company:** Marble - **Location:** Inconnu, France - **Experience:** Expert - **Salary:** €36,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Collaborative Learning, Supervisory Control and Data Acquisition (SCADA), Machine Learning, OPC Unified Architecture, Software Deployment, Reinforcement Learning, Machine Learning Operations, Automation Anywhere, GXP - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/co-founder-cto-physical-ai-for-process-industries-marbleclimate-8017510 ## About the Role We are looking for an entrepreneurial Applied AI/ML Engineer who can turn ambiguous problems into a technical vision and operational reality. You have a driving motivation to build the future of industrial process plant operations. Must have: * Deep ML expertise across at least two of physics-informed models, time-series, reinforcement learning, or agentic systems; and the judgement to choose the right tool not the fanciest. * Comfortable turning dirty data into reliable training sets through sensor data curation, plant-graph contextualisation, and integration with semi-structured records. * AI-native by default, leveraging agents, automations, and AI workflows, and are ready to drive that culture as the company scales. You can architect and build our platform MVP from the ground up. You have deployed ML products in messy, real-world environments. You understand commissioning, safety constraints, and human-in-the-loop workflows. The ideal candidate will have prior experience working in or alongside process industries, and a hands-on understanding of industrial data stacks (OPC UA / SCADA / MES / ERP / historians), data compliance (GDPR, SOC 2, ISO 27001) and industrial safety frameworks (IEC 61508, ATEX, GxP). Above all, the candidate should have an entrepreneurial mindset and a clear willingness to take ownership in a co-founder role. Location: We are primarily seeking candidates based in Europe, with a preference for France or Switzerland, with the ability to travel to project sites across the region when necessary. Examples of relevant backgrounds. * Senior Applied AI/ML Engineer or Forward Deployed Engineer who's deployed ML systems for industrial customers at a world-leading tech company or industrial AI startup * AI/ML Research engineer or scientist from an applied ML team with a track record of deploying and validating solutions in production, for example working on physical AI * Startup founder / operator with technical leadership experience in applied ML and production deployments in industrial environments You're encouraged to apply even if your background isn't a perfect match. Above all, we value intelligence, creativity, and founder potential. ## Description We have identified a key entry point that has previously been overlooked. Where customer pain is high, and deployment is fast. Solving it delivers immediate savings across energy, water, chemicals, and downtime. And we're already signing paid pilots. This early use case allows us to build the foundations of our platform. From there, our approach extends to adjacent unit operations within the plant and across industries, with the long-term vision of a unified layer that optimises process plant operations in real time. At scale, we address a €300B market, reduce industrial energy spend by 15%, and tackle 4% of global CO emissions. We are looking for a CTO and Co-Founder with world-class ML engineering experience who wants to apply their talent to improving the physical world. You will take full ownership of the technical direction and execution, from early pilots to full-scale deployment. You will shape the platform models and architecture (data curation & contextualisation, physics models, ML pipelines, operator-facing apps), focused on building a robust, replicable product. As we scale, you will build the engineering team and set the bar for technical excellence. You will join our current Founder in Residence and CEO: a PhD in process engineering from ETH Zurich, with experience working on the factory floor across pulp & paper, waste-to-energy, cement, and food production. He has developed hybrid physics-ML models and optimised production lines. He brings deep knowledge of both the physics and the customer, and an obsession with making the industry better., * €250,000 inception funding at incorporation, if you are successful. You and your team will own 80% of the company at incorporation. * Full-stack support in our venture creation programme + Mentorship and guidance at every step of the way + Hands-on support across technology and market research, landing interviews with experts and customers, techno-economic modelling, roadmap and scale-up strategy, sourcing your future co-founders and advisors, and more + Access to an unparalleled network across science, industry, talent, investors, mentors, and other climate deeptech founders + Access to our research insights, knowledge base, and curated resources + Weekly check-ins, work sessions in our Paris HQ (or remote), and continuous peer learning with other programme participants * Seed fundraising support after the programme. Introductions to the best seed investors and help with term sheet negotiation. * Founder visa sponsorship if you wish to relocate to France. Remote participation would be a challenge, but we're open to finding different partnership formats. ## Related Videos - [100 times more frequent deployments: How did we create a high performance team?](https://www.wearedevelopers.com/videos/1069-100-times-more-frequent-deployments-how-did-we-create-a-high-performance-team) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Industrial AI: Built for reality, operation, and people](https://www.wearedevelopers.com/videos/2074-industrial-ai-built-for-reality-operation-and-people) - [From Space to Software: Reliability Lessons 40 Years After Challenger](https://www.wearedevelopers.com/videos/100283-from-space-to-software-reliability-lessons-40-years-after-challenger) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Where To Find Software Engineering Jobs](https://www.wearedevelopers.com/magazine/396-where-to-find-software-engineering-jobs) - [Find a Developer Job: 12 Best Job Sites For Developers](https://www.wearedevelopers.com/magazine/165-find-a-developer-job-12-best-job-sites-for-developers)