Principal Engineer, AI Acceleration

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About the role

Job Description About Us Core IT is responsible for delivering and maintaining the technology, platforms, and services that help teams across GM do their best work. We support product, design, engineering, manufacturing, operations, and corporate functions by improving the tools, workflows, and technology experiences they rely on every day. Our goal is to provide secure, reliable, and seamless technology that helps employees spend less time navigating friction and more time solving the problems that matter most in their domains. The Role At General Motors, we are using AI to simplify how work gets done—reducing manual effort, removing friction, and helping engineers and business teams focus on higher-value outcomes. As a Principal Engineer, AI Acceleration, you will identify high-leverage opportunities, then design, build, and scale AI-powered tools that measurably improve how people work. You will operate at the intersection of software engineering, AI/LLM capabilities, product thinking, and organizational change. This is a hands-on technical leadership role. This isn’t a research or advisory role. You’ll ship tools people use every day and aim for step changes of 10x to 100x, not incremental gains. For example, our design team’s brand guidelines live in Figma. Instead of maintaining static templates, we built an AI skill that reads the guidelines directly, drafts on-brand presentations, and runs a linter that catches off-brand choices. Work that took days now takes minutes, and the guidelines stay the single source of truth. You’d find and build the next dozen of these. You will work directly with product, design, engineering, manufacturing, operations, and corporate-function teams to understand the problems behind the requests. You will move quickly from discovery and prototyping to production-quality solutions, while maintaining appropriate standards for security, privacy, reliability, responsible AI, and supportability. The right person is comfortable creating clarity where there is no specification, switching between executive-level strategy and technical implementation, and measuring success by outcomes. What You’ll Do • Find high-leverage opportunities: Embed with teams across GM to identify slow, manual, repetitive, or error-prone work where AI can create a step-change in speed, quality, or user experience. • Prototype and deliver quickly: Build and test working solutions, validate value with users, and move successful prototypes into reliable, maintainable, supported production capabilities. • Build on approved platforms: Develop solutions using GM-approved AI and enterprise technologies, integrating with the systems where work already happens, such as Google Workspace, Figma, Jira, enterprise data platforms, and other relevant services. • Apply strong engineering discipline: Establish appropriate practices for requirements, architecture, testing, observability, security, privacy, access control, deployment, incident response, and lifecycle management. Partner with security, legal, privacy, and risk stakeholders to set practical guardrails for safe and responsible AI use. • Measure business impact: Define success metrics and evaluate time saved, quality, adoption, cycle time, cost, and user satisfaction. Retire or revise solutions that do not produce meaningful outcomes. • Scale what works: Convert successful implementations into reusable reference architectures, patterns, components, documentation, and enablement that other teams can adopt. Help teams adopt new ways of working and build the capability to extend solutions after initial delivery. • Provide technical leadership: Mentor engineers, raise the quality bar through design and code reviews, and communicate tradeoffs and recommendations to technical and

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