Intel

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.

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.

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.

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.

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.

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.

The role involves developing Triton front-end and back-end components for Intel GPUs, focusing on creating efficient custom GPU kernels for AI workloads. Responsibilities include defining, designing, developing, testing, and maintaining software tools for domain-specific programming languages, working with hardware design teams and compiler development communities, and participating in language standards groups. The ideal candidate has experience in GPU programming for AI, C/C++/Python, compiler stages, code generation, optimization, and GitHub. Familiarity with PyTorch attention techniques for transformer models is also required.

AI Software Development Engineer focused on optimizing AI inference workloads (LLMs, Diffusion models) on Intel GPUs. This role involves end-to-end optimization across graph compilation, runtime execution, and low-level GPU kernels, requiring strong C++ skills and understanding of GPU architectures and neural network inference.

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.

AI Software Solution Engineer focused on validation and workload enabling for Intel platforms. The role involves optimizing AI model efficiency, accuracy, and performance by working with frameworks, algorithms, and hardware. Key responsibilities include enabling AI models on Intel GPUs, debugging deep learning models, conducting benchmarking and validation, developing automation pipelines, and evaluating AI models against competitors. The role also involves customer engagement for enablement and performance improvements, and translating AI workload needs into architecture insights.

Seeking a Senior AI Algorithm Engineer to develop and optimize oneDNN, a critical open-source performance library for deep learning applications, enabling state-of-the-art neural network performance across Intel hardware (CPUs, GPUs). The role involves low-level performance engineering, parallel algorithm development, and contributing to the open-source community.

Engineer to work on Intel's AI frameworks software stack, focusing on design, development, and optimization of features for AI accelerators and GPUs. This includes ML kernel development, enhancing training and inference capabilities, and contributing to open-source AI frameworks like PyTorch, Tensorflow, and JAX.

Lead Senior Design Engineer focused on AI SoC development, responsible for defining, implementing, and validating complex SoC IP blocks and subsystems for AI applications. This role involves architectural leadership, microarchitecture and RTL development, verification collaboration, timing/physical design support, and silicon bring-up, all while ensuring power, performance, and security requirements are met for next-generation AI solutions.

Senior System Debug Engineer responsible for the design and development of integrated AI solutions for deep learning and machine learning systems, focusing on hardware, software, firmware, board, and silicon components. The role involves AI systems architecture, defining product specifications, and impacting the AI product roadmap. It requires developing new methods in various AI/ML domains, leading design and implementation of component-level choices for performance and cost, defining system integration approaches, and delivering end-to-end technical solutions. The role also includes debugging and ensuring the reliability of AI infrastructure, collaborating on next-generation requirements, and influencing AI roadmap with customer knowledge.

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.

This role focuses on designing and developing features for Intel's AI frameworks software stack, specifically optimizing inference serving frameworks (like SGLang, vLLM) and ML frameworks (PyTorch, Tensorflow, JAX) for Intel's AI accelerators and GPUs. The engineer will enhance deep learning training and inference capabilities, identify optimization opportunities, and contribute to open-source communities.

Engineer to design and develop features for Intel's AI frameworks software stack, focusing on inference serving frameworks (SGLang, vLLM) and ML frameworks (PyTorch, Tensorflow, JAX). The role involves optimizing software for Intel's AI accelerators and GPUs, enhancing training and inference capabilities, and contributing to open-source communities.

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.

Software engineer focused on implementing and optimizing generative AI workloads (LLMs, diffusion models) on Intel GPUs using the OpenVINO inference runtime. The role involves analyzing performance bottlenecks, adapting state-of-the-art techniques, and optimizing for current and future GPU architectures, requiring deep C++ and system-level expertise.

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.

Product Manager for AI Compute Software, focusing on performance-critical middleware for AI training and inference on accelerators and CPUs. The role involves defining product strategy, roadmap, and execution, collaborating with hardware, framework, and system software teams to ensure high performance, scalability, and developer usability.

Software Development Engineer focused on optimizing Neural Network performance for Intel AI accelerators using compiler technologies and graph theory. The role involves driving design, implementing compilation passes, and developing performance analysis tools for future AI hardware.

AI Frameworks Engineer focused on optimizing generative AI models for efficient inference using the OpenVINO toolkit across Intel hardware, from edge to cloud. This role involves deep dives into generative model architectures and the OpenVINO ecosystem, implementing new features, and optimizing performance for state-of-the-art inference solutions.

Senior AI SoC Architect role at Intel focused on architecting next-generation datacenter AI SoCs and rack-scale AI servers. Responsibilities include defining and leading architecture development, influencing direction, and collaborating across disciplines for optimized power/performance. Requires 10+ years of SoC architecture experience.

Senior Design Verification Engineer for Intel's Neuromorphic Computing Lab, focusing on pre-silicon verification of novel AI accelerator architectures. The role involves developing a scalable verification framework, writing directed and random tests, debugging, and using AI tools to improve productivity. Experience with UVM/OVM and AI tools for test generation is required.

Systems Research Engineer/Scientist role focused on leveraging AI/ML for higher efficiency and performance in system architecture innovations, including high-performance cluster computing, virtualization, and accelerated computing. The role involves prototyping, characterizing, and analyzing workloads, developing tools for performance assessment, and influencing future product roadmaps. Requires strong systems knowledge and hands-on experience with AI workloads, with a focus on performance modeling and analysis of AI inference or training.

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.

Software development engineer to work on oneDNN project, a complex cross-platform open-source software project focusing on neural network performance. oneDNN is a critical component of Intel AI strategy, powering key AI applications. Role involves design, development, and maintenance of new functionality in oneDNN to enable performance critical portions of AI workloads, supporting software developers optimizing AI frameworks and workloads for Intel CPUs and GPUs.

Lead a digital design team developing cutting-edge AI SoCs, focusing on RTL coding, PPA analysis, and cross-functional collaboration for high-end chip design.

This role is for an End-to-End GPU AI Software Architect at Intel, focusing on the entire software stack from firmware to applications to ensure Intel GPU AI software meets product requirements for workloads, performance, and stability. The architect will make key technical design decisions, influence technical direction across the industry, and mentor other technical leaders.

Seeking a Verification Team Leader for Intel's AI group in Israel to manage a team, develop verification plans, oversee verification environments, and collaborate with design teams for cutting-edge AI hardware products. Requires 10+ years in VLSI chip verification, 4+ years in leadership, and experience with SoCs and AI applications.

Software Enabling and Optimization Engineer role focused on optimizing software for Intel products, including AI frameworks, across various domains like Cloud, AI, HPC, Gaming, Graphics, and Edge computing. Responsibilities include development, integration, testing, debugging, and optimization of software, collaborating with customers and partners, evangelizing Intel's tools, and researching innovative solutions. Familiarity with AI frameworks and performance optimization is preferred.

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.

Internship role focused on building AI software stacks, GPU programming, and performance optimization. Contributes to design, development, and optimization of AI software solutions, algorithms, frameworks, and architectures. Assists in implementing and tuning models for performance and accuracy, applied research, and hardware-software integration. May involve creating AI software solutions and system-level deployment for scalable and efficient AI.

Internship role supporting the design and development of integrated AI solutions combining software, hardware awareness, and system-level concepts. Focus on learning AI systems development across the tech stack, from models to platform considerations, and contributing to prototyping and testing.

This intern role focuses on the hardware engineering aspects of AI ecosystem solutions, involving algorithm and framework design, AI software architecture, and optimizing AI solutions for hardware performance. It combines hardware engineering with AI/ML techniques for design efficiency and analysis, including implementing and tuning models, applied research, and system-level deployment. The role emphasizes AI augmenting engineering judgment.

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.

Internship role focused on supporting the development and optimization of integrated AI solutions, collaborating with hardware and software teams to meet business and customer needs. Involves performance benchmarking, data analysis, and applying AI frameworks to refine solutions for AI use cases.

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.