At Schneider Electric, we are committed to solving real-world problems to create a sustainable, digitized, new electric future.

Artificial Intelligence has the potential to transform industries and help unlock efficiency and sustainability.

Within our Global AI Hub we combine our long-standing manufacturing and domain expertise with cutting-edge innovation in AI, machine learning, and deep learning to empower smarter decision-making, agility, and decarbonization.

Our Strategy & Innovation team drives the AI strategy and innovation efforts for the AI Hub, Schneider Digital, and Schneider Electric at large.

We are building the next generation of intelligent systems that combine large-scale multimodal models, graph/topology understanding, simulation, and domain logic to enable system-level reasoning across energy, buildings, industry, and data centers.

Your role : We are looking for an applied research engineer with strong foundations in ML/AI and a deep interest in reasoning, agents, planning, and tool-augmented intelligence.

You will design novel reasoning architectures that combine foundation-model outputs with system structure, simulations, and domain rules - building prototypes that demonstrate robust, verifiable reasoning for real industrial systems.

Your responsibilities : Design novel reasoning methods that combine LLM/FM outputs with graphs, causal structure, and constraints to solve multi-step, system-level tasks Prototype agents/planners that can query tools (simulators, rule engines, optimizers) and self-check with constraints/physics before proposing actions Fuse multimodal signals - time-series, tabular, text, graph (vision/3D later)—into coherent world-state representations and uncertainty-aware decisions Establish evaluation for reasoning (task success, counterfactuals, causal sanity checks, safety/constraint satisfaction) and run clean ablations/baselines Collaborate with domain experts to encode topology, procedures, and invariants into the reasoning layer Your profile : Required Qualifications PhD in ML/AI/CS (or related) with strong grounding in transformers/LLMs and reasoning/representation learning.

We are also open to exceptional candidates from top Master’s programs or leading engineering schools with demonstrated research or applied excellence 3+ years of industry experience applying ML/AI methods to real-world problems, including prototyping, experimentation, or integrating models into practical workflows Evidence of research in at least two of the following: Causal & probabilistic modeling (interventions, counterfactuals, structure learning) Graph/topology reasoning (GNNs, message passing, graph-of-thought) Tool-augmented agents / planning (planning/search, tool calling, RAG for tools) Physics/constraints-aware learning (PIML, rule integration, optimization under constraints) Hands-on skill to turn ideas into prototypes (Python, Py Torch), with clean experimentation (baselines, ablations, reproducibility) Preferred Skills

Experience: integrating simulators/solvers/rule engines into model loops (safety checks, feasibility, constraint satisfaction) Familiarity with knowledge graphs and multimodal alignment (time-series/tabular/text/graph/vision) Exposure to neuro-symbolic or hybrid approaches (neural + rules/logic) for interpretability and robust generalization Comfort collaborating with SMEs to embed topology, procedures, and safety policies into reasoning What We Offer The opportunity to shape the reasoning core of next-gen industrial multimodal AI - with real impact on energy, industry, and sustainability Access to rich, real-world datasets (time-series, tabular, text, graph; vision later) and domain simulators rarely available in academia A fast-moving, collaborative, research-driven environment - prototype, evaluate, publish when appropriate A mission tying AI research to physical systems and decarbonization at global scale Looking to make an IMPACT with your career?

When you are thinking about joining a new team, culture matters.

At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success.

We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us. IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play.

It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.

We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization.

We celebrate IMPACT Makers and believe everyone has the potential to be one.

Become an IMPACT Maker with Schneider Electric – apply today! €36 billion global revenue +13% organic growth 150 000+ employees in 100+ countries #1 on the Global 100 World’s most sustainable corporations You must submit an online application to be considered for any position with us.

This position will be posted until filled.

Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best.

We mirror the diversity of the communities in which we operate, and ‘inclusion’ is one of our core values.

We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.

At Schneider Electric, we uphold the highest standards of ethics and compliance, and we believe that trust is a foundational value.

Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders.

You can find out more about our Trust Charter here Schneider Electric is an Equal Opportunity Employer.

It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

At Schneider Electric, we are committed to solving real-world problems to create a sustainable, digitized, new electric future.

Artificial Intelligence has the potential to transform industries and help unlock efficiency and sustainability.

Within our Global AI Hub we combine our long-standing manufacturing and domain expertise with cutting-edge innovation in AI, machine learning, and deep learning to empower smarter decision-making, agility, and decarbonization.

Our Strategy & Innovation team drives the AI strategy and innovation efforts for the AI Hub, Schneider Digital, and Schneider Electric at large.

We are building the next generation of intelligent systems that combine large-scale multimodal models, graph/topology understanding, simulation, and domain logic to enable system-level reasoning across energy, buildings, industry, and data centers.

Your role : We are looking for an applied research engineer with strong foundations in ML/AI and a deep interest in reasoning, agents, planning, and tool-augmented intelligence.

You will design novel reasoning architectures that combine foundation-model outputs with system structure, simulations, and domain rules - building prototypes that demonstrate robust, verifiable reasoning for real industrial systems.

Your responsibilities : Design novel reasoning methods that combine LLM/FM outputs with graphs, causal structure, and constraints to solve multi-step, system-level tasks Prototype agents/planners that can query tools (simulators, rule engines, optimizers) and self-check with constraints/physics before proposing actions Fuse multimodal signals - time-series, tabular, text, graph (vision/3D later)—into coherent world-state representations and uncertainty-aware decisions Establish evaluation for reasoning (task success, counterfactuals, causal sanity checks, safety/constraint satisfaction) and run clean ablations/baselines Collaborate with domain experts to encode topology, procedures, and invariants into the reasoning layer
Your profile : Required Qualifications PhD in ML/AI/CS (or related) with strong grounding in transformers/LLMs and reasoning/representation learning.

We are also open to exceptional candidates from top Master’s programs or leading engineering schools with demonstrated research or applied excellence 3+ years of industry experience applying ML/AI methods to real-world problems, including prototyping, experimentation, or integrating models into practical workflows Evidence of research in at least two of the following: Causal & probabilistic modeling (interventions, counterfactuals, structure learning) Graph/topology reasoning (GNNs, message passing, graph-of-thought) Tool-augmented agents / planning (planning/search, tool calling, RAG for tools) Physics/constraints-aware learning (PIML, rule integration, optimization under constraints) Hands-on skill to turn ideas into prototypes (Python, Py Torch), with clean experimentation (baselines, ablations, reproducibility) Preferred Skills

Experience: integrating simulators/solvers/rule engines into model loops (safety checks, feasibility, constraint satisfaction) Familiarity with knowledge graphs and multimodal alignment (time-series/tabular/text/graph/vision) Exposure to neuro-symbolic or hybrid approaches (neural + rules/logic) for interpretability and robust generalization Comfort collaborating with SMEs to embed topology, procedures, and safety policies into reasoning What We Offer The opportunity to shape the reasoning core of next-gen industrial multimodal AI - with real impact on energy, industry, and sustainability Access to rich, real-world datasets (time-series, tabular, text, graph; vision later) and domain simulators rarely available in academia A fast-moving, collaborative, research-driven environment - prototype, evaluate, publish when appropriate A mission tying AI research to physical systems and decarbonization at global scale