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 protocols) and communicate findings clearly
- Solid engineering discipline: Git, PRs, code reviews, reproducibility, experiment tracking, and collaborative development practices
Preferred Skills- Interest or familiarity with engineering, energy, or physical systems - curiosity about real-world technical domains is a strong plus
- Exposure to simulation-based learning, physics-aware models, or neuro-symbolic approaches
- Comfortable moving between research and applied prototyping, turning ideas into working demos
- Contributions to open-source projects, workshops, or scientific publications
What We Offer- The opportunity to shape the next generation of multimodal AI systems for energy, industry and sustainability
- Access to rich, real-domain multimodal datasets, rarely available in academic or tech environments
- A role at the intersection of AI research, physical systems understanding and sustainability, working on problems with real impact
- A fast moving, collaborative, and deeply technical team, embedded within Schneider Electric's global AI strategy and innovation ecosystem
