Intel currently has 84 active job listings related to artificial intelligence. The majority of these roles, 51%, are focused on serving infrastructure, with agents representing another significant portion at 24%. Engineering is the most frequent function for these positions. The company is actively hiring in the United States, China, and Mexico. Frequent technical tags include model serving, inference infrastructure, and agent orchestration. In the last 30 days, Intel has added 73 new AI roles, representing a 52% increase compared to the previous 30-day period.
Currently tracking 56 active AI roles, down 14% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $122k–$414k (avg $253k).
Intel currently has 67 active AI-related roles in our index. The most common open titles are: AI Software Engineering Intern (4), GenAI Software Solutions Engineer (3), AI Software Engineer Intern (2), Graduate Talent (GenAI Software Solutions Engineer) (2), Middleware Development Engineer (2). Most positions are in Engineering and Research.
Intel's active AI hiring is concentrated in: serving infrastructure (51%), agents (27%), application (9%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Intel is hiring AI talent in: United States (20 roles), China (12 roles), Mexico (9 roles), Malaysia (8 roles).
Job postings at Intel most frequently reference: model serving, inference infra, agent orchestration, rag, fine tuning.
In the past 30 days, Intel has posted 43 new AI-related roles. That is a -45% change versus the prior 30 days (78 → 43).
| Title | Stage | AI score |
|---|---|---|
| Systems Research Engineer/Scientist Systems Research Engineer/Scientist role focused on leveraging AI/ML for higher efficiency and performance in system architecture innovations, including high-performance cluster computing, virtualization, and accelerated computing. The role involves prototyping, characterizing, and analyzing workloads, developing tools for performance assessment, and influencing future product roadmaps. Requires strong systems knowledge and hands-on experience with AI workloads, with a focus on performance modeling and analysis of AI inference or training. | Serve | 7 |