Intel
- HQ
- Santa Clara, US
- Founded
- 1968
- Size
- 120,000+
- Website
- intel.com
Intel currently has 59 active AI-related roles in our index. The most common open titles are: AI Software Engineering Intern (3), AI Software Engineer Intern (2), GenAI Software Solutions Engineer (2), Graduate Talent (GenAI Software Solutions Engineer) (2), AI Algorithm Engineer. Most positions are in Engineering and Research.
Intel's active AI hiring is concentrated in: serving infrastructure (49%), agents (29%), application (8%). 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 (28 roles), China (7 roles), Mexico (6 roles), Malaysia (6 roles).
Job postings at Intel most frequently reference: model serving, inference infra, agent orchestration, rag, tool use.
In the past 30 days, Intel has posted 28 new AI-related roles. That is a -63% change versus the prior 30 days (75 → 28).
Currently tracking 56 active AI roles, down 27% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $122k–$414k (avg $253k).
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.
| Title | Stage | AI score |
|---|---|---|
| Senior GenAI Software Solutions Engineer Senior GenAI Software Solutions Engineer role focused on architecting, building, and optimizing hybrid AI agents that run across device and cloud environments. Responsibilities include MCP service integration, agentic routing and planning, model runtime engineering, security and compliance, and optimization techniques like quantization and distillation. Requires 5+ years in AI/ML algorithm development and 2+ years in NLP, LLM-based systems, or AI agent development. | Agent | 8 |
| GenAI Software Solutions Engineer This role focuses on designing, building, and integrating generative AI agents and platforms using both local and cloud-based LLMs. The engineer will develop AI-powered products, focusing on agent orchestration, tool usage, RAG, and system optimization, translating AI logic into production-quality software. The position is for a fresh graduate or early-career professional with basic understanding of GenAI and LLMs, and programming experience in Python/C++. |
| Agent |
| 7 |
| Graduate Talent (GenAI Software Solutions Engineer) This role focuses on designing, building, and integrating generative AI agents and platforms, leveraging both local and cloud-based LLMs. The engineer will develop AI-powered products and solutions for real-world use cases, focusing on agent orchestration, tool usage, RAG, and system optimization. Responsibilities include end-to-end development from implementation to deployment, translating AI logic into production-quality software. | Agent | 7 |
| GenAI Software Solutions Engineer This role focuses on designing, building, and deploying generative AI agents and platforms, integrating local and cloud-based LLMs. It involves developing AI-powered products for intelligent reasoning and automation, with an emphasis on agent orchestration, tool usage, RAG, and system optimization. The position requires end-to-end software development, including implementation, testing, debugging, documentation, and deployment, for a fresh graduate or early-career engineer. | Agent | 7 |
| Graduate Talent (GenAI Software Solutions Engineer) This role focuses on designing, building, and integrating generative AI agents and platforms, leveraging both local and cloud-based LLMs. The engineer will develop AI-powered products and solutions for real-world use cases, focusing on agent orchestration, tool usage, RAG, and system optimization. Responsibilities include end-to-end development, translating AI logic into production software, and deploying AI services. | Agent | 7 |