AI Research Scientist

Schneider Electric•Andover, Massachusetts

63d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

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