Intel

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.

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.

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.

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.

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.

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.

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.

This role focuses on optimizing AI software solutions for Intel's AI PC environments by collaborating with customers and ecosystem partners. The engineer will develop, integrate, test, tune, and debug software, leveraging tools like OpenVINO, Llama.CPP, Ollama, LMStudio, and vLLM, to enhance product adoption and differentiation. Key responsibilities include researching and prototyping software, evangelizing Intel's tools, leading pre-enabling efforts, and identifying key workloads for future product designs.

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.

Internship role focused on optimizing AI software solutions, including algorithms, frameworks, and architectures for computer vision, machine learning, and deep learning. Responsibilities include researching model quantization and graph transformation, evaluating LLM performance on Intel platforms, analyzing software bottlenecks, and assisting in implementing and tuning AI models for performance and accuracy. The role emphasizes hardware-software integration and collaboration for scalable AI solutions.

This internship focuses on optimizing core GPU compute kernels for AI and numerical workloads, validating GPU IP with AI inference and training workloads, and performing GPU performance profiling and analysis. The role involves hardware/software codesign for next-generation Intel GPU and AI accelerator platforms.

Intern role focused on optimizing GPU compute kernels for AI workloads and validating GPU IP. Involves performance profiling, analysis, and modeling to improve next-generation Intel GPU and AI accelerator platforms.

This internship focuses on optimizing core GPU compute kernels for AI and numerical workloads, validating GPU IP with AI inference and training workloads, and performing GPU performance profiling and analysis. The role involves hardware/software codesign for next-generation Intel GPU and AI accelerator platforms.

Internship role focused on the research and application of Vision-Language-Action (VLA) algorithms in robot scenarios. Responsibilities include VLA data collection, model fine-tuning, performance testing, problem analysis, and optimization using Python and frameworks like lerobot, PyTorch, and TensorFlow. The role involves improving model adaptability and execution accuracy for robot systems.

Intern role focused on optimizing CPU kernels for AI workloads, including LLMs and multimodal models, using Intel architecture features and performance profiling tools. Integrates custom operators into production frameworks.

Internship role focused on developing efficient algorithm solutions for accelerating large AI models and agentic systems, with an emphasis on deployment in resource-constrained computing platforms.

AI Compiler and Library Engineer Intern at Intel, contributing to the design, development, and optimization of AI software solutions, including algorithms, frameworks, and architectures. Focuses on implementing and tuning models for performance and accuracy, applied research, and hardware-software integration, with potential involvement in system-level deployment. The role emphasizes learning and skill development through hands-on projects supporting Intel's business goals.

AI Software Engineering Intern at Intel contributing to the design, development, and optimization of AI software solutions across computer vision, machine learning, and deep learning. Responsibilities include implementing and tuning models, conducting applied research, and assisting with system-level deployment.

AI Software Engineering Intern focused on designing, developing, and optimizing AI software solutions, including algorithms, frameworks, and architectures. Key responsibilities include tuning deep learning models, exploring model compression techniques (quantization, pruning), and conducting applied research for system-level deployment and hardware integration. The role emphasizes practical engineering applications and inference optimization.

AI Software Engineer Intern role focused on the design, development, and optimization of AI software solutions, including algorithms, frameworks, and architectures. Responsibilities include implementing and tuning models, applied research, hardware-software integration, and system-level deployment. The role involves learning and applying knowledge in areas like computer vision, machine learning, and deep learning, with a focus on performance and accuracy.

This intern role focuses on optimizing deep learning models and their deployment for Intel GPUs/CPUs. Responsibilities include performance tuning, debugging accuracy and memory issues, developing deployment frameworks (e.g., using vLLM), and creating high-performance kernels. The role involves technical syncs with architects and transforming innovative ideas into production-ready features.

AI Software Engineering Graduate Intern to contribute to the development and optimization of AI workload solutions spanning algorithms, AI software frameworks, system software, and infrastructure. Responsibilities include reproducing AI inference workloads, building workload profiles, identifying system bottlenecks, and performing performance analysis to support hardware/software co design for next generation AI SoC products.

Intel CAIGC is seeking an AI Framework Engineer to advance AI technologies and software ecosystems, focusing on enabling the AI PC ecosystem and delivering full-stack GPU IP solutions. The role involves research, validation, performance evaluation, and optimization of AI models and frameworks on Intel platforms.