Principal Engineer, AI Acceleration

GM • Warren, MI

1d • Remote

About The Position

General Motors Core IT delivers and maintains technology, platforms, and services used by teams across GM. The Principal Engineer, AI Acceleration will identify high-leverage opportunities and design, build, and scale AI-powered tools that measurably improve how employees work. This is a hands-on technical leadership role at the intersection of software engineering, AI and LLM capabilities, product thinking, and organizational change—not a research or advisory role. The engineer is expected to ship useful tools and pursue substantial improvements in speed, quality, or user experience.

The role works directly with product, design, engineering, manufacturing, operations, and corporate-function teams to understand underlying problems, move from discovery and prototyping to production-quality solutions, and maintain appropriate standards for security, privacy, reliability, responsible AI, and supportability. It calls for creating clarity amid ambiguity, shifting between strategic and implementation work, and measuring success through outcomes.

The position is based remotely. Candidates living within a specified radius of a GM hub are expected to report to that location three times per week or at another frequency set by their manager. Relocation benefits are available to candidates who qualify under company policy. GM does not provide immigration-related sponsorship for this role.

Requirements

• 10+ years of progressive software engineering experience, including delivery of production software used by customers or employees.
• Experience serving as a Principal Engineer, senior technical leader, or equivalent, with influence across multiple teams or organizations.
• Hands-on experience designing and building AI- or LLM-enabled applications, agents, or automations across a wide range of AI tools and models.
• Strong understanding of LLM application patterns, including prompting, retrieval-augmented generation, tool use, context management, model selection, evaluation, and observability.
• Experience integrating AI solutions with enterprise applications, APIs, data sources, identity systems, and workflow platforms.
• Strong software engineering fundamentals, including architecture, APIs, distributed systems, testing, CI/CD, monitoring, security, and operational excellence.
• Ability to translate ambiguous business problems into clear product and technical strategies and deliver working solutions through iterative development.
• Ability to use data and user feedback to prioritize work and evaluate adoption, quality, and measurable impact.
• Demonstrated success helping people and teams adopt AI tools and continue using them after initial rollout.
• Strong communication and collaboration skills, including the ability to influence executives, technical experts, and frontline users.
• Comfort operating autonomously, making decisions with incomplete information, and taking accountability for outcomes.
• High standards for quality and craft, with attention to whether results are genuinely good, not merely functional.

Nice To Haves

• Experience building internal developer platforms, productivity tools, workflow automation, enterprise AI products, or employee-facing software.
• Experience with agentic systems, Model Context Protocol (MCP), AI skills, orchestration, evaluation frameworks, or multi-model solutions.
• Experience with Glean, Claude, Gemini, Figma AI, Google Workspace, Jira, or comparable enterprise AI and productivity platforms.
• Experience establishing AI engineering standards, reusable patterns, governance, or responsible-use practices in a large organization.
• Experience measuring developer or employee productivity through outcomes such as cycle time, quality, adoption, satisfaction, or cost avoidance.
• Experience mentoring senior engineers and building communities of practice.
• Experience with creative or design workflows, enterprise data systems, or manufacturing and automotive environments.

Responsibilities

• Embed with teams across GM to identify slow, manual, repetitive, or error-prone work where AI can significantly improve speed, quality, or user experience.
• Build and test working solutions, validate value with users, and move successful prototypes into reliable, maintainable, supported production capabilities.
• Develop solutions using GM-approved AI and enterprise technologies, integrating with systems such as Google Workspace, Figma, Jira, enterprise data platforms, and other relevant services.
• 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 establish practical guardrails for safe and responsible AI use.
• Define success metrics and evaluate time saved, quality, adoption, cycle time, cost, and user satisfaction; revise or retire solutions that do not produce meaningful outcomes.
• Turn successful implementations into reusable reference architectures, patterns, components, documentation, and enablement for other teams.
• Help teams adopt new ways of working and build the capability to extend solutions after initial delivery.
• Mentor engineers, raise the quality bar through design and code reviews, and communicate tradeoffs and recommendations to technical and executive audiences.

Benefits

• Relocation benefits are available for candidates who qualify under company policy.
• GM provides well-being support at work and at home; details are available through its Total Rewards resources.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed