AI Research Scientist

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 and their post-training to enable system-level reasoning across energy, buildings, industry, and data centers.

Your role :

We're looking for a curious, fast-moving applied AI research scientist (Official Title: Data Scientist) who loves working on cutting-edge innovation projects and transforming it into prototypes.

You will drive the development of multimodal AI systems that power real-world energy and industrial decisions at scale.

The right candidate will combine strong fundamentals in foundation models with rigorous experimentation, solid engineering habits, and an end-to-end maker mindset - from preparing the data to building the model to crafting demos that make the value visible.

Thrive in a collaborative environment, engage actively with the research community, and enjoy working with product and business teams to translate ideas into real impact.

Your responsibilities:


* Advance state-of-the-art research for core modalities - time series, tabular, text, and graph/topology, visual/3D data


* Rapidly translate state-of-the-art research into prototypes, adapting multimodal and transformer-based architectures to Schneider-specific datasets


* Build robust, reproducible ML pipelines, covering data preparation, experiment tracking, baselines, ablations


* Lead the creation and preparation of multimodal datasets, transforming raw data (such as time-series signals, structured tables, documents, diagrams, and system relationships) into clean, usable training datasets


* Collaborate with domain experts and product teams to align modeling choices with physical constraints and convert prototypes into clear, impactful demonstrations

Required Qualifications


* PhD in Machine Learning, Artificial Intelligence, NLP, Robotics, or a related field, with strong foundations in transformers and modern representation learning.

Candidates with a Master's degree and a track record of outstanding research or applied impact are also encouraged to apply.


* Demonstrated experience in foundation models and post-training


* Strong hands-on experience with PyTorch, custom model architectures, and efficient training/finetuning methods


* Ability to design clean, rigorous experiments (baselines, ablations, evaluation protoco...