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.

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.

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.

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.

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 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.

Intern role focused on optimizing GPU compute kernels for AI workloads and validating GPU IP. Involves performance profiling, analysis, and building performance models to understand architecture-level behavior, contributing to hardware/software codesign for next-generation Intel GPUs and AI accelerators.

Research intern focusing on system reliability (RAS) and silent data error characterization and mitigation for AI and general-purpose compute platforms, including heterogeneous systems and large-scale server clusters. Responsibilities include designing and running experiments, analyzing logs, and prototyping detection/diagnosis methods to improve data integrity and platform robustness across the HW/FW/OS/runtime stack.

This internship focuses on supporting the design, deployment, and troubleshooting of Cloud and AI systems and solutions, collaborating with hardware and software teams to optimize for real-world use cases. The role involves applying AI framework knowledge and systems engineering principles to ensure reliability and scalability.

AI Performance Engineer Intern at Intel focused on analyzing silicon chip performance for deep learning, conducting large-scale benchmarks, designing automation tools for data collection and analysis, and researching new architectural features for GPUs, CPUs, and SoCs. The role involves system-level modeling, testing, characterization, and performance-per-watt analysis, with a strong emphasis on understanding deep learning models and frameworks.

Research Intern focusing on system innovation, cost optimization, and GPU interconnect protocols for disaggregated AI supernode architectures. The role involves exploring architectural innovations, implementing distributed memory pooling, and researching Ethernet-native GPU interconnect protocols for large-scale AI inference and training clusters. Familiarity with RDMA, Mellanox tools, and LLM inference benchmarking methodologies is required.

Internship role focused on the design, development, and optimization of AI software solutions, including algorithms, frameworks, and architectures. The role involves implementing and tuning models for performance, applied research, and hardware-software integration, with potential for system-level deployment. Familiarity with PyTorch, model profiling, and optimization is a plus.

Internship role focused on the design, development, and optimization of AI software solutions, including algorithms, frameworks, and architectures. The role involves implementing and tuning models for performance, applied research, and hardware-software integration, with potential for system-level deployment. Familiarity with PyTorch, model profiling, and optimization is a plus.

This role focuses on optimizing web runtime performance for Intel AI PCs by enabling and optimizing key Chromium components, including Web AI features, on Intel hardware (CPU, GPU, NPU). The engineer will work with internal hardware teams and the Chromium open-source community.

Cloud Software Development Engineer at Intel, focusing on optimizing software stacks for Intel hardware in cloud environments. Specializations include data services, AI/ML, or open-source development. Responsibilities involve designing, developing, validating, and debugging software solutions, with a focus on performance optimization and customer support.