Staff Developer/Architect – Next Gen AI @ General Motors

Job DescriptionWork Arrangement: This role is categorized as hybrid. This means the successful candidate is expected to report to the office, three days per week, at minimum. But if you live outside an 80 KM radius from Markham Elevation Centre, it will be remote. Why Join UsGeneral Motors is at the forefront of transforming transportation through software-driven innovation. We’re driven by our bold vision of a future with Zero Crashes, Zero Emissions, and Zero Congestion. As we push forward into an era of vehicle intelligence and digital engineering, AI is a cornerstone of our strategy.You’ll be part of a team that is pioneering the integration of simulation, automation, AI agents, and large language models (LLMs) into critical systems for vehicle design, calibration, and performance. We’re seeking a Staff AI Developer to serve as a key technical leader, shaping the evolution of our tools and infrastructure while delivering scalable, intelligent solutions that drive real-world engineering impact.Role OverviewAs a Staff AI Developer, you will operate as a senior technical expert and strategic contributor within a growing AI-focused engineering team. You will architect and deploy scalable AI systems—particularly in the areas of LLMs, AI agents, retrieval-augmented generation (RAG), and hybrid AI-simulation models—that enable transformative use cases across simulation, calibration, and product development.You will work cross-functionally with engineers, data scientists, simulation specialists, domain experts and platform teams to define and execute high-impact AI initiatives. Your role will blend hands-on development, technical direction-setting, and mentorship, helping GM scale next-generation capabilities.Key ResponsibilitiesTechnical Leadership & InnovationArchitect, prototype, and productionize scalable AI systems, with an emphasis on LLMs, simulation-aware models, and hybrid AI pipelines.Lead AI integration into core engineering tools and simulation frameworks, ensuring robustness, interpretability, and physical relevance of outputs.Evaluate and define the appropriate use of RAG systems, fine-tuning vs. zero/few-shot learning strategies, and feedback loops for…

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