Machine Learning Engineer

About us

Over the past few months, and with a founding team consisting of Julien, a PhD in materials science, and Katie, a former business development manager at a climate technology company, we have raised our first round of funding from leading investors, secured several letters of intent and demonstrated that we have a way of boosting the world's production of recycled copper.

We have developed a new wire-forming technology that produces high-conductivity copper wire without the need for 99.95% pure copper raw materials. No smelting. No refining. Just a smarter process.

By eliminating these costly and highly polluting steps, we are considerably reducing the overall cost of copper wire production, while making use of millions of tonnes of copper scrap currently unsuitable for electrical use. Our technology paves the way for fully circular and cost-effective copper wire production, making it an essential part of the global energy transition.

We’re now building our core team, and it’s the perfect time to jump on board.

About the role

We are looking for a Machine Learning Engineer with a strong background in deep learning for predictive process control, to help us model and optimize our novel mechanical and thermal wire forming process.

You’ll take the lead on developing a model-based controller capable of anticipating system behavior and driving performance improvements in real-time. Your work will sit at the intersection of physical process modeling, data-driven algorithms, and real-world industrial systems.

• Contract: CDI (permanent)
• Location: Nanterre (hybrid)
• Language: English fluency required. French is a plus but not required.

Your responsibilities

• Develop and deploy predictive control algorithms to optimize mechanical and thermal processes for wire forming
• Build hybrid models using deep learning and physics-based insights, incorporating process data and literature-based proxies
• Design and implement efficient data collection pipelines from characterization methods to support model training and validation, to gather an optimized dataset
• Simulate process dynamics, stress-test control logic, and run live tests on physical prototypes
• Ensure the system is robust to uncertainties and ready for industrial scale-up

Ideal background

• Engineering degree, MSc or PhD in Machine Learning, Control Engineering, Applied Mathematics, Physics, Material Science or related field
• Strong hands on experience in model-based controlling and reinforcement learning for process control/material characterization
• Proven track record applying deep learning to real-world physical systems
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, C++)
• Familiarity with simulation tools (e.g., Abaqus, Ansys, Simulink, Modelica, or custom simulation environments)
• Bonus: Experience working with manufacturing systems or industrial process control
• Bonus: Demonstrated initiative in building and experimenting, whether through side projects, small-scale physical prototypes, or hands-on tinkering in a garage or lab

What we offer

We’re building a world-class industrial deep tech company, and we’re doing it from the ground up. You’ll be one of the earliest team members, shaping not only the tech, but the culture, direction, and impact of our start-up.

• CDI contract
• Competitive compensation + BSPCE package
• 50% health insurance coverage
• Hybrid work model (office in Nanterre)

We believe deep tech needs deep diversity. We’re committed to building an inclusive, impact-driven team, and if you’re excited by our mission but don’t tick every box, we’d still love to hear from you. We value versatility and the ability to learn, which is absolutely essential at this stage of our business.

Let’s build the future of circular copper, together.