GM Cuts Around One In Ten IT Salaried Roles, Seeks Staff With Stronger Machine-Learning Skills

GM Is Reworking Its Tech Ranks — Letting Go Of About 600 Office Workers To Clear Space For Engineers Focused On Model Building, Data Pipelines And Cloud-Based AI

General Motors has trimmed about one-tenth of its salaried information-technology workforce, a reduction that freed roughly 600 positions as the automaker shifts toward hiring people with deeper machine-learning and data engineering expertise. The company confirmed the changes to reporters and said the move is part of a broader reorganization of its technology group aimed at concentrating resources on strategic priorities.

Insiders say many of the departures are intended to make room for different kinds of talent rather than cut the IT footprint permanently. GM is recruiting engineers who can design and train models, assemble scalable data pipelines, deploy cloud-native systems and develop autonomous agents and model-driven workflows. In short, the firm is prioritizing staff who can architect AI systems end to end, not only employees who use AI tools to improve day-to-day productivity.

The shake-up continues a pattern of personnel shifts at GM over the past year and a half. In August 2024 the company reduced its software engineering ranks by about 1,000 positions. Organizational changes accelerated after the May 2025 hire of Sterling Anderson as chief product officer, a move that coincided with several senior software executives leaving the company late last year. GM has since added a number of external hires with AI and autonomy backgrounds, including an AI lead with prior experience at a major consumer tech firm and a new vice president for autonomous vehicles who previously led AI work at Cruise.

Industry analysts say the strategy reflects how large corporations are responding to advanced computing: companies are not merely bolting on new tools, they're remaking teams so software and data specialists sit at the center of development. “Firms that want to build production machine-learning systems need engineers who understand models, data architecture and cloud operations,” said a former tech executive who consults with automakers. “You can’t always retrain people quickly enough, so some roles disappear while new ones appear.”

The personnel changes carry risks as well as potential gains. Hiring for machine-learning specialties is competitive, and integrating new hires into a legacy auto maker’s product cycle can be difficult. Some employees affected by the cuts said they were offered transition support, while others are seeking roles elsewhere in the industry. GM faces the twin tasks of recruiting scarce talent and reskilling or redeploying entrenched teams if it hopes to accelerate software-driven vehicle features.

For the auto industry, GM’s choices point toward growing demand for skills tied to agent development, model operations and ML-first engineering practices. As automakers turn more of their systems into software projects — from driver assistance to vehicle services — companies that build robust model training and deployment capabilities will likely gain an edge.