Intel

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.

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.

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.

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.

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.

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.

GPU Software Engineer focused on AI-driven software development and validation, building and optimizing software quality measurement and tracking systems. Responsibilities include developing high-performance software modules with AI technologies and models, optimizing media drivers, and deploying LLMs/VLMs within agentic frameworks.

Robotics Research Intern at Intel focusing on advanced algorithmic development and robotics research for next-generation robotic technologies. The role involves researching, designing, and optimizing robotics algorithms, control systems, and AI/ML models, with a focus on enabling intelligent autonomous systems and innovative robotic applications. Collaboration with cross-functional teams to translate research into practical implementations is key.

Intern role focused on the development, validation, and deployment of AI tools such as GenAI assistants, RAG-based knowledge tools, and workflow automation agents within an enterprise AI context. Requires basic knowledge of ML/GenAI concepts and Python programming.

Research intern position focused on Confidential AI systems and Agent-CC solutions for hyperscale, involving LLMs, AI systems, and microarchitecture. Requires programming, foundational computer architecture, and AI math skills.

Image Processing Engineer with C++/Linux experience to develop and implement computer vision algorithms for nanometer-scale metrology applications in mask and semiconductor manufacturing. The role involves algorithm development, programming, software tool development, testing, documentation, and user interaction to improve quality control and process development.

Sr. Security Architect role focused on applying AI-driven tools to enhance security architecture for Client and Data Center SoCs, including firmware and low-level hardware/software. The role involves using AI for vulnerability identification, code analysis, threat modeling, and defining security specifications, aiming to discover risks earlier and more broadly than traditional methods.