Job offer — France

AI R&D Engineer

Nantes, France · Full-time (CDI) · Starting March/April 2026

We are deepmath, a deep-tech startup headquartered in Nantes, France, with operations in Brazil and strategic partnerships worldwide. We are excited to announce a CDI position for an AI R&D Engineer, starting March/April 2026, focused on developing advanced machine learning techniques with an emphasis on transformers and graph neural networks. If you are driven by innovation and motivated to tackle challenging problems in deep learning, we invite you to apply and join our journey!

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About us

We combine cutting-edge techniques in mathematical modeling, physics-based simulation, and artificial intelligence to deliver accurate descriptions and predictions of physical phenomena. Although we support projects in wind, solar, and offshore energy, our main focus is developing next-generation engineering simulation tools, where advanced physics and AI converge to tackle demanding industrial challenges.

deepmath is building a diverse, healthy, and supportive work environment where creativity thrives. We believe that great ideas emerge when people feel respected, supported, and empowered. Our goal is to create the conditions for every team member to grow, express their strengths, and reach their full potential.

The project

Engineering simulation has long faced a critical bottleneck: the generation of high-quality meshes, especially for Computational Fluid Dynamics (CFD). For decades, this process has remained largely manual, time-consuming, and dependent on expert knowledge, making it one of the main obstacles to the large-scale industrial adoption of advanced simulation tools.

Our project, deepmesh, addresses this challenge by developing an intelligent AI-driven mesh generator. By combining graph neural networks and transformer architectures, deepmesh learns to automatically produce simulation-ready meshes for complex geometries and demanding physical conditions. This technology enables faster, more reliable, and more accessible simulations, allowing engineers to focus on innovation rather than preprocessing, and laying the foundation for next-generation engineering design and optimization workflows.

Your missions

As an AI R&D Engineer, you will contribute directly to the evolution of our intelligent mesh generation system. Working closely with our CTO Bruno and our tech-dev team, you will help design, implement, and optimize the machine learning models that power deepmesh. Your role spans model architecture, data processing, large-scale training, and experimental validation. Your missions will include:

  • Developing and improving AI models based on graph neural networks and transformer architectures;
  • Designing and maintaining efficient data-loading, preprocessing, and batching pipelines for large-scale datasets;
  • Running extensive training experiments, analyzing results, and iterating to improve model accuracy, robustness, and performance;
  • Contributing to model optimization, training scalability, inference efficiency, and memory management;
  • Collaborating with the team to integrate new model components into the broader deepmesh pipeline.

Your profile

Hard skills

  • Graduate degree (MSc or PhD) in Computer Science, Applied Mathematics, Engineering, or a related field, with work involving deep learning or scientific machine learning;
  • Solid experience with modern deep learning architectures, especially transformers and graph neural networks;
  • Strong programming skills in Python and PyTorch and proficiency with Linux environments. Experience with simulation tools is a plus;
  • Experience with model development pipelines: dataloading, preprocessing, supervised training, evaluation, and optimization;
  • Experience with large-scale training on GPUs or cloud/HPC environments;
  • Proactivity and the ability to work independently while maintaining effective communication;
  • Fluency in English (working language of the team).

Soft skills

  • Passion for mathematical modeling, physics, and computer science;
  • Eagerness to take initiative in a fast-moving startup environment;
  • Excellent problem-solving and critical thinking skills.

Your professional outcomes

By joining deepmath, you will play a key role in shaping the development of deepmesh and advancing AI methods for engineering simulation. Your work will directly influence the performance of our technology and the success of our projects. Concretely, this means:

  • Driving innovation in deep learning methods for mesh generation and simulation workflows;
  • Seeing your contributions integrated into a technology used across industry and research;
  • Working in a startup environment where your ideas directly influence technical direction and product strategy;
  • Leading projects from initial concept to integration in real simulation pipelines.

A vibrant and supportive work environment

We foster a workplace atmosphere that encourages well-being, creativity, and collaboration. As part of our team, you can expect:

  • Your ideas and contributions to be valued. Our culture promotes open exchange and supports every team member in achieving collective goals;
  • Flexibility to maintain a healthy balance between work and personal life, with schedules adapted to your needs and remote work options;
  • Opportunities to stay at the forefront of your field through access to learning resources, conferences, and workshops.

How to apply

If you are ready to make a meaningful impact by advancing the frontiers of AI in engineering simulation, we would be delighted to hear from you. Apply with your CV and a cover letter (PDF, in English) describing your background, motivations, and what inspires you to pursue this opportunity.

We look forward to getting to know you better — The Founders.

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Website publisher: deepmath solutions, SAS with share capital of 25,000 EUR.

Registered office: 12 RUE OLIVIER, 44100 NANTES, France.

SIREN: 982 168 940
SIRET: 982 168 940 00012
RCS: 982 168 940 R.C.S. Nantes
VAT number: FR09982168940

Publication director: Liad Paskin, President.

Contact: liadpaskin@deepmath.tech

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