Currently tracking 56 active AI roles, down 34% 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.
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).
| Title | Stage | AI score |
|---|---|---|
| AI Research and Development Engineer (Physical AI) This role focuses on building the end-to-end stack for robotic intelligence, bridging high-level research and low-level performance optimization for physical AI. It involves training large-scale Vision-Language-Action (VLA) models and optimizing them for real-time deployment on edge hardware, including developing safety runtimes and model-agnostic inference APIs. | Post-trainServe | 9 |
| Neuromorphic/AI Research Scientist Research Scientist role focused on developing and benchmarking AI algorithms for Intel's next-generation neuromorphic architecture, targeting edge computing, signal processing, and autonomous systems. The role involves implementing and evaluating algorithms on neuromorphic hardware, validating the SDK, and presenting findings. |
| 9 |
| Neuromorphic/AI Research Scientist Research Scientist role focused on advancing neuromorphic computing technology for commercial adoption, involving algorithm design, prototyping, and evaluation for applications in robotics, signal processing, and autonomous systems on edge platforms. The role requires translating research breakthroughs into real-world products and collaborating with hardware/software teams. | ShipData | 9 |
| Neuromorphic Applications Researcher- Temporary Position Research role focused on developing and benchmarking AI algorithms and robotics applications for Intel's next-generation neuromorphic architecture, aiming to enable physical AI systems with groundbreaking performance and efficiency. The role involves evaluating applications on neuromorphic hardware, validating the SDK, and publishing results. | ShipServe | 9 |
| Senior AI Software Architect - Runtime Intel is seeking a Senior AI Software Architect to lead the development of their neuromorphic AI execution stack for edge and robotic systems. This role involves architecting and optimizing firmware, runtime components, and performance infrastructure, integrating the stack into robotics ecosystems, and providing technical leadership. The position requires extensive experience in low-level systems software for AI accelerators, software architecture, and production-grade software development in C++/Python, with a strong background in AI/deep learning workloads. | ServeShip | 9 |
| AI Software Engineer Intern Internship role focused on applied research and productization of Vision-Language Models (VLM) and Vision-Language-Action (VLA) models, including pre-training, fine-tuning, alignment, data pipelines, fusion strategies, action components, and model optimization for efficient deployment on Intel hardware. The role involves evaluating models and potentially publishing results. | Post-trainData | 9 |
| AI Software Engineer Intern This role focuses on building and optimizing a next-generation LLM inference system, including model optimization, inference runtime, and system-level design. It involves research and engineering to implement and optimize core techniques across the stack from model to kernels to runtime to distributed systems, with a key focus on GPU kernel and runtime optimization for an end-to-end AI rack software system for LLM inference. | Serve | 9 |
| AI Software Engineer Intern This role focuses on building and optimizing a next-generation LLM inference system, including model optimization, inference runtime, and system-level design. It involves research and engineering to implement and optimize core techniques across the stack from model to kernels to runtime to distributed systems, with a key focus on GPU kernel and runtime optimization for an end-to-end AI rack software system for LLM inference. | Serve | 9 |
| AI Algorithm Research Intern – Neuromorphic Computing AI Algorithm Research Intern focused on developing, implementing, and benchmarking algorithms for Intel's next-generation neuromorphic architecture to enable applications in edge computing, signal processing, and autonomous systems. The role involves contributing to Intel's neuromorphic SDK and publishing research findings. | Data | 9 |
| AI Algorithm Research Intern – Neuromorphic Computing Intern position at Intel's Neuromorphic Computing Lab focused on developing, implementing, and benchmarking algorithms for next-generation neuromorphic architectures. The role involves supporting application development, publishing research, and contributing to the neuromorphic SDK, with a focus on edge computing, signal processing, and autonomous systems. | Data | 9 |
| Machine Learning Engineer Machine Learning Engineer/Data Scientist role focused on agent harness research and model fine-tuning, involving building evaluation benchmarks, iterating on agent harness components (context, memory, tools, skills), developing and maintaining post-training pipelines, designing RL environments and reward functions, and optimizing training runs. The role emphasizes the intersection of research and engineering for agentic applications. | Post-trainAgent | 8 |
| AI Systems and Solutions Engineering Manager Leads a team of engineers responsible for the design and development of integrated end-to-end hardware and/or software systems for AI products and autonomous systems, including deep learning hardware structures. Oversees the development of large-scale continuous delivery systems for AI product development and influences the AI product roadmap based on deep understanding of AI/DL algorithms and customer requirements. Drives strategy for AI research capabilities and manages teams to execute through clear goal setting and accountability. | Ship | 8 |
| Principal Engineer, AI Systems and Solutions Principal Engineer, AI Systems and Solutions at Intel, focusing on designing and developing integrated AI systems (hardware, software, firmware, silicon) for industries like autonomous driving, robotics, and cloud. The role involves leading AI systems architecture, defining components, and innovating in areas like reinforcement learning, computer vision, and simulation, with a strong emphasis on productization and end-to-end solution delivery. | Ship | 8 |
| AI Software Engineering Intern This internship focuses on developing and enabling Agentic AI systems on AI PC platforms. Responsibilities include setting up AI environments, experimenting with AI models and applications, and supporting pretraining and task learning pipelines. The role emphasizes hands-on engineering across AI infrastructure, system setup, and agent-oriented AI workloads, contributing to client AI software ecosystem development. | AgentPost-train | 8 |
| AI Software Development Engineer AI Software Development Engineer focused on building and scaling production-grade data infrastructure for agentic AI systems. Responsibilities include agent orchestration, data engineering and integration, DevOps for AI agent systems, and observability/evaluation for agent runs. Requires experience in DevOps, SRE, data engineering, or infrastructure engineering for production AI or distributed systems, LLM serving, RAG, and Python. | Agent | 8 |
| AI Software Solutions Engineer Senior AI Software Solutions Engineer focused on integrating and validating Vision AI and Gen AI solutions on Intel hardware. Responsibilities include performance optimization, benchmarking, technical advising, and customer integration, with a focus on the OpenVINO toolkit and edge AI. | ServeAgent | 8 |
| GenAI Software Architect GenAI Software Architect role focused on building and optimizing machine learning products and solutions, particularly LLM-based systems and AI agents. Requires deep expertise in GenAI algorithms, solution architecture, performance tuning, and experience with frameworks like LangChain, RAG pipelines, and vector databases. The role involves developing custom AI tools and optimizing GenAI workloads for edge devices. | Agent | 8 |
| 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 |
| Firmware validation AI intern This intern role focuses on AI agent development, including designing autonomous agents, integrating tools and APIs, building RAG systems, orchestrating multi-agent collaboration, and implementing performance monitoring. It requires familiarity with agent frameworks like LangChain and an understanding of LLM architectures, prompt engineering, and RAG techniques. The role also involves traditional firmware validation tasks like test case development and issue investigation. | Agent | 8 |
| Senior GenAI Software Architect Senior GenAI Software Architect role focused on building and architecting machine learning products and solutions, with a strong emphasis on GenAI algorithms, LLM-based systems, and AI agent development. The role involves translating ML models into software, optimizing for edge devices, and supporting customer/partner deployments. | AgentPost-train | 8 |
| Embodied AI Robot System Intern Develops and integrates large models (LLMs/VLMs/VLAs) into ROS 2-based robotic systems for perception, planning, and execution. Designs reward functions, training curricula, and evaluation protocols for embodied tasks, with a focus on training RL policies for manipulation. | AgentData | 8 |
| Data Science Student for AI Solutions Group Intel's AI Solutions Group is seeking an MSc/PhD student to work on state-of-the-art AI capabilities for chip development. The role involves solving high-value problems using ML, DL, and LLMs, from ideation and research to preparing solutions for deployment. Requires strong Python, ML/DL knowledge, and familiarity with AI tools like PyTorch or Scikit-learn. | Post-train | 8 |
| Applied Machine Learning Engineer (LLMs & RL) Applied Machine Learning Engineer focused on fine-tuning large language models (LLMs) and Reinforcement Learning (RL). Responsibilities include designing and maintaining post-training pipelines, developing RL environments and reward models, debugging and scaling distributed training, and designing experiments and evaluation metrics. | Post-trainAgent | 8 |
| GPU Power Architect The role focuses on designing and developing energy-efficient hardware architectures for AI/ML workloads, specifically for GPUs. Responsibilities include building and validating GPU power models, optimizing for performance-per-watt, and developing scalable power analysis flows. The position requires a strong background in computer architecture, digital logic design, and power modeling. | Serve | 8 |
| Principal Engineer: XeSS and Neural Graphics Principal Engineer to drive Intel's XeSS and related AI-based graphics technologies, impacting XeSS Super Resolution, Frame Generation, Neural Rendering, and next-gen AI rendering. The role involves shaping technical direction, driving execution across research, software, hardware, validation, and ecosystem teams, and bringing AI graphics technologies from concept to product. Responsibilities include end-to-end development across model design, datasets, training, visual quality, performance optimization, and product integration, as well as guiding the application of modern AI model architectures to future graphics workloads. | ShipServe | 8 |
| AI Algorithm Engineer Scientist AI Algorithm Engineer Scientist at Intel focused on generative AI, specifically for building next-generation code generation agents for GPU programming. The role involves research and development of ML models, algorithm optimization for CPUs/GPUs, and translating models into deployable products, with a focus on areas like audio, voice, speech, and vision processing. | AgentData | 8 |
| Principal Engineer – Distributed AI Systems Architecture (Heterogeneous Compute) Seeking a Principal Engineer to architect next-generation distributed AI systems across heterogeneous compute platforms (CPUs, GPUs, accelerators). The role focuses on dynamic execution of large-scale AI computation graphs, managing state, locality, and performance. Responsibilities include defining runtime models, stateful scheduling, graph introspection, integrating specialized accelerators, MoE-aware execution, and adaptive runtime optimization. Requires deep expertise in systems architecture, HPC, distributed systems, and heterogeneous compute environments, with experience in AI/ML systems and inference infrastructure preferred. | ServeAgent | 8 |
| Research and Pathfinding Internship: AI Workload Compiler Optimization for CPU and GPU Internship role focused on advancing compiler infrastructure for heterogeneous AI workloads by developing novel optimization techniques for AI kernel compilation targeting both CPU and GPU architectures using MLIR/LLVM. Explores algebraic optimization, hierarchical scheduling, and cost-driven pruning for high-performance fused kernels. | Serve | 8 |
| Senior GenAI Software Architect Senior GenAI Software Architect role focused on building and architecting machine learning products and solutions, with a strong emphasis on GenAI algorithms, LLM-based systems, and AI agent development. The role involves translating ML models into software, optimizing for edge devices, and supporting customer/partner deployments. | AgentPost-train | 8 |
| AI Frameworks Software Engineer – Model Compression Algorithm Develop Intel Neural Compressor product and related tools, optimize for Intel AI platform (CPU, GPU, AI Accelerator). Research and implement quantization and compression techniques for LLMs and text-to-image/video generation models. Track and explore cutting-edge directions in efficient model deployment and inference/finetuning acceleration. | ServePost-train | 8 |
| Physical AI Engineer The Physical AI Engineer role at Intel focuses on designing and developing integrated AI solutions for deep learning and machine learning systems, encompassing hardware, software, firmware, and silicon. The role involves AI systems architecture, defining product specifications, and impacting the AI product roadmap. Key responsibilities include developing new methods in areas like reinforcement learning, computer vision, and robotics, leading design and implementation of AI systems, and delivering end-to-end technical solutions for customer problems. The role also involves analyzing AI infrastructure reliability and collaborating on next-generation requirements. | ShipData | 8 |
| GenAI Software Architect GenAI Software Architect role at Intel, focusing on building and optimizing AI/ML-based products and solutions, particularly LLM-based systems and AI agents. Requires expertise in GenAI algorithms, solution architecture, performance tuning, and experience with frameworks like LangChain and RAG pipelines. The role involves developing and deploying machine learning models and methods into software, with a focus on real-world use cases and edge device optimization. | Agent | 8 |
| Data Scientist Data Scientist role focused on accelerating pre and post silicon validation using AI/ML. Responsibilities include designing and deploying ML algorithms and generative AI pipelines, architecting end-to-end AI systems (data pipelines, training, inference, MLOps), developing advanced AI models for debug efficiency, and applying LLMs/RAG for log summarization and triage automation. The role requires strong Python, ML framework, SQL, and software engineering skills, with preferred experience in validation environments, transformer models, LLM fine-tuning, and RAG. | AgentData | 8 |
| Neuromorphic Applications Engineer- (Temporary Position) This role focuses on demonstrating the value of Intel's neuromorphic technologies by developing, implementing, and benchmarking algorithms for next-generation neuromorphic architectures. The goal is to enable applications in edge computing, signal processing, and autonomous systems for physical AI, with a focus on robotics applications like VLA models for drones and humanoids. The role involves validating the neuromorphic SDK, gathering metrics, proposing software enhancements, and presenting findings. It's a fixed-term position within Intel's CTO Office, aiming to commercialize neuromorphic technology. | ShipServe | 8 |
| AI Robotics Engineer- (Temporary Position) Develop, implement, and benchmark advanced robotics algorithms optimized for modern heterogeneous Intel compute architectures, enabling high performance and efficient solutions for real-world applications in autonomous systems, edge robotics, and intelligent physical systems. This role involves integrating and simulating large-scale robotics systems and bringing real robotic platforms to life, with a focus on commercializing these technologies for future Intel and partner products. | ShipAgent | 8 |
| Senior Principal Engineer – AI Applied Research Senior Principal Engineer in AI Applied Research at Intel, focusing on applying AI/ML to logic IP design and semiconductor manufacturing. The role involves conducting applied research, developing proof-of-concept models, and implementing solutions to demonstrate business value, requiring expertise in deep learning, ML, RL, NLP, GNNs, and time-series. The position emphasizes leadership, influencing partners, and mentoring technical leaders. | Post-train | 8 |
| AI Software Engineer – Agentic AI System AI Software Engineer focused on building infrastructure and tooling for an end-to-end evaluation ecosystem for agentic AI frameworks. Responsibilities include developing scalable deployment and orchestration systems, data pipelines, dashboards, observability tooling, and automating evaluation pipelines for AI agent frameworks and model-serving systems. | AgentEval Gate | 7 |
| Software Engineer - Agent Harness Software Engineer to build the 'harness' for agentic AI systems, focusing on the agent loop, tool integration, context management, and scheduling. The role emphasizes creating a model-agnostic framework for hybrid local and cloud intelligence, with a strong focus on core product engineering for an agent framework. | Agent | 7 |
| Inference Optimization Engineer (local / edge runtime) This role focuses on optimizing AI inference engines (like llama.cpp, vLLM) for constrained local and edge hardware, including GPUs/iGPUs and Vulkan backends. The goal is to improve latency, throughput, and memory usage for interactive agent workloads, driving quantization strategies and reducing CPU overhead. This is crucial for making hybrid, low-cost agent products viable. | Serve | 7 |
| Triton Compiler Engineer Develops Triton front-end and back-end components for Intel GPUs, focusing on creating efficient custom GPU kernels for AI workloads. Requires strong programming skills in C, C++, Python, and experience with compiler stages, code generation, and optimization techniques. | Serve | 7 |
| AI Algorithm Engineer AI Algorithm Engineer role focused on designing, building, and integrating generative AI agents and platforms using local and cloud-based LLMs. The role involves developing AI-powered products, focusing on agent orchestration, tool usage, RAG, and system optimization, and translating AI logic into production-quality software. | Agent | 7 |
| AI Software Engineering Intern Internship role focused on the design, development, and optimization of AI software solutions, including algorithms, frameworks, and architectures. Involves implementing and tuning models, applied research, and hardware-software integration, with potential for system-level deployment. Aims to provide hands-on experience in supporting Intel's business goals. | ServePost-train | 7 |
| AI Framework Software Engineer AI Framework Software Engineer at Intel focused on designing, developing, and optimizing AI software and frameworks. The role involves implementing distributed algorithms, transforming computational graphs, developing ML/DL primitives, profiling models, and optimizing code for hardware backends. Collaboration with ML/DL researchers is key. | Serve | 7 |
| Graduate Talent (VDI & Media AI Systems Engineering) This role focuses on optimizing and evaluating AI workloads, particularly for VDI and Media AI systems, on Intel Xeon CPU platforms and AI accelerators. Key responsibilities include benchmarking inference performance, supporting AI PoCs, enabling GPU-accelerated inferencing, and creating automation pipelines for AI service deployment. The role involves working with AI frameworks and AI workloads like Vision-Language Models, Generative AI, and Agentic AI, with a focus on inference performance and resource utilization. | ServeAgent | 7 |
| Senior AI SoC Design Engineer This role is for a Senior AI SoC Design Engineer at Intel, focusing on developing hardware for AI applications. The engineer will own the architecture and end-to-end design of complex SoC subsystems, drive RTL design, and collaborate across various engineering disciplines. A strong understanding of AI/ML workloads and their impact on hardware architecture is required, along with expertise in SystemVerilog and microarchitecture. The role involves driving system-level PPA tradeoffs and mentoring senior engineers, contributing to the next-generation SoC and AI architecture roadmap. | Serve | 7 |
| AI Software Engineer AI Software Performance Engineer focused on optimizing AI solutions and workloads on Intel products, involving analysis, prototyping, model modification, and performance benchmarking. | ServePost-train | 7 |
| GenAI Software Solutions Engineer Designs and builds generative AI agents and platforms using local and cloud LLMs, focusing on agent orchestration, tool usage, RAG, and system optimization. Translates AI logic into production software with end-to-end development responsibilities. Role is entry-level with basic understanding required. | Agent | 7 |
| 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 |