Senior Researcher, AI​/ML Battery Modeler

Senior Researcher, AI/ML Battery Modeler

Hybrid:
This role is categorized as hybrid. Successful candidate is expected to report to the Research Administration Building at GM Global Technical Center in Warren, MI at least 3 times per week.

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard - from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.

Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.

• Work alongside globally recognized experts across battery materials, modeling, and systems engineering
• Tackle open-ended, high-impact problems spanning electrochemistry, thermal science, mechanics, data science, and AI
• Leverage cutting-edge experimental facilities, advanced instrumentation, and high-performance computing
• Use state-of-the-art AI/ML and scientific modeling tools to generate new insights
• Be supported to publish and present original research in leading journals and top conferences
• Collaborate with battery suppliers, tier 1 research universities, national labs, and DOE/NSF-funded programs
• See your research directly implemented in GM EVs at a global scale
• Grow across technical, leadership, and research career paths in a deeply technical environment

As a Senior Researcher, you will shape the future of electric mobility by developing next-generation battery models using advanced AI/ML techniques. You’ll build physics-informed, data‑driven, and hybrid models—while defining new modeling approaches that push the boundaries of battery science and AI.

• Develop and deploy advanced AI/ML models, including physics-informed and hybrid approaches, to predict battery performance, aging, and degradation
• Build and refine Multiphysics models that couple electrochemical, thermal, and mechanical behavior
• Identify and develop novel modeling approaches for next‑generation battery chemistries and systems
• Turn complex experimental and simulation datasets into actionable insights using Python-based scientific computing and ML frameworks
• Partner with engineering teams to translate research into production, integrating models into battery management systems (BMS) and product workflows
• Publish and present your work through peer‑reviewed journals, conferences, and technical forums
• Drive technical direction through deep problem solving, collaboration, and influence across disciplines
• Contribute to intellectual property and patents for new algorithms and modeling innovations

• Master’s or Ph.D. in Electrical Engineering, Physics, Computer Science, Materials Science, Chemical Engineering, or related field with a focus on AI/ML
• Proven experience developing AI/ML models, ideally applied to batteries, electrochemical systems, or Multiphysics domains
• Strong Python proficiency (required) with experience in ML/scientific libraries (e.g., PyTorch, Tensor Flow, Num Py, Sci Py)
• Strong analytical skills with the ability to extract insight from complex datasets
• Demonstrated research experience (e.g., publications, technical reports, or conference presentations)
• Ability to clearly communicate complex technical concepts to diverse audiences
• Strong collaboration and problem‑solving skills, with the ability to influence across teams
• Commitment to continuous learning and advancing the state of the art in AI/ML and battery technology

• Experience with physics-informed or hybrid modeling approaches
• Background in battery degradation, aging, or lifetime modeling
• Familiarity with battery management systems (BMS) or system‑level integration
• Experience with large‑scale datasets or high-performance computing environments
• Contributions to patents, intellectual property, or external research collaborations
• Experimental experience…