GPU compute performance engineer at Intel Corporation

📍 Job Overview

Job Title: GPU Compute Performance Engineer
Company: Intel Corporation
Location: Leixlip, Kildare, Ireland
Job Type: Experienced Hire
Category: Hardware Engineer - GPU Compute
Date Posted: June 18, 2025
Experience Level: 2-5 years
Remote Status: On-site

🚀 Role Summary

AI-GPU Architecture Team: Leverage insights from AI workloads to shape future Intel GPU design decisions.
Performance Engineering: Identify bottlenecks, drive hardware/software fixes, and optimize GPU performance.
Collaboration: Work cross-functionally with hardware, software, and partner teams to deliver high-performance solutions.
Innovation: Contribute to both hardware and software innovations, growing into a performance architect position.

📝 Enhancement Note: This role offers an exciting opportunity for experienced GPU performance engineers to make a significant impact on Intel's AI-GPU roadmap and architecture.

💻 Primary Responsibilities

Workload Analysis & Performance Debugging: Identify and resolve performance bottlenecks through hardware and software fixes.
AI-GPU Hardware Architecture Evaluation: Influence the product roadmap by understanding AI algorithms, customer needs, and software frameworks.
Tool Development: Develop and enhance internal performance analysis tools to streamline debugging and optimization processes.
Software Stack Development: Collaborate with partner teams to develop highly optimized GPU kernels and software stacks.
Solution Support: Provide a comprehensive view of solutions and support both pre- and post-silicon activities.
Research & Academic Partnerships: Collaborate with experts to analyze next-generation requirements and guide research and academic partnerships.

📝 Enhancement Note: This role requires a strong understanding of AI workloads, GPU architecture, and performance analysis techniques to drive meaningful improvements in GPU performance.

🎓 Skills & Qualifications

Education: Master's degree in Computer Engineering, Computer Science, Electrical Engineering, Mathematics, or a related field.

Experience: 2-3 years of experience in GPU/CPU architecture for AI workloads.

Required Skills:

Proficient in Python, C/C++
Strong data analysis and presentation skills
Knowledge of AI and deep learning, including Large Language Models (LLM) and Stable Diffusion (preferred)

Preferred Skills:

Experience in performance analysis/performance debugging
Experience in building analytical and/or simulation-based performance models
Knowledge/experience in CPU, GPU, or memory design/architecture, and/or microarchitecture/RTL/design/process technologies
An aptitude to learn new things quickly and adapt to a fast-paced hardware/software development environment

📝 Enhancement Note: Candidates with a strong foundation in GPU architecture, AI workloads, and performance analysis will thrive in this role and have the opportunity to grow into a performance architect position.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate a strong understanding of GPU architecture and AI workloads through relevant projects and case studies.
Showcase your ability to identify and resolve performance bottlenecks in GPU-based systems.
Highlight your experience with performance analysis tools and techniques.
Include examples of your work that showcase your proficiency in Python, C/C++, and other relevant programming languages.

Technical Documentation:

Document your approach to performance analysis, including data collection, analysis, and optimization strategies.
Include code snippets and examples that illustrate your problem-solving skills and technical expertise.
Prepare a portfolio that showcases your ability to work effectively in a collaborative, cross-functional team environment.

📝 Enhancement Note: A well-curated portfolio that demonstrates your technical skills and ability to drive GPU performance improvements will be crucial for success in this role.

💵 Compensation & Benefits

Salary Range: €65,000 - €85,000 per year (based on market research for experienced hardware engineers in Ireland with relevant skills)

Benefits:

Competitive healthcare and retirement benefits
Employee stock purchase plan
Generous time-off policies, including vacation, sick leave, and holidays
On-site fitness centers and wellness programs
Tuition reimbursement and professional development opportunities
Employee assistance programs and counseling services

Working Hours: Full-time (40 hours/week), with flexibility for project deadlines and maintenance windows

📝 Enhancement Note: Intel offers competitive compensation and benefits packages for experienced hardware engineers, with a focus on work-life balance and professional development opportunities.

🎯 Team & Company Context

🏢 Company Culture

Industry: Semiconductor and technology manufacturing

Company Size: Large (over 100,000 employees)

Founded: 1968

Team Structure:

AI-GPU Architecture Team: Focuses on leveraging insights from AI workloads to shape future Intel GPU design decisions.
Cross-functional collaboration with hardware, software, and partner teams to deliver high-performance solutions.

Development Methodology:

Agile development processes, with a focus on continuous integration and collaboration.
Regular code reviews, testing, and quality assurance practices.
Deployment strategies, CI/CD pipelines, and server management to ensure optimal performance and reliability.

Company Website: Intel Corporation

📝 Enhancement Note: Intel's large size and established presence in the semiconductor industry offer experienced hardware engineers ample opportunities for growth and collaboration with diverse teams.

📈 Career & Growth Analysis

Web Technology Career Level: Experienced Engineer - GPU Compute

Reporting Structure: Reports directly to the AI-GPU Architecture Team Lead, with regular collaboration with hardware, software, and partner teams.

Technical Impact: Contributes to the development and optimization of Intel's AI-GPU hardware architecture, influencing the product roadmap and driving performance improvements.

Growth Opportunities:

Grow into a performance architect position, contributing to both hardware and software innovations.
Expand technical expertise in AI workloads, GPU architecture, and performance analysis techniques.
Develop leadership skills through mentoring, team management, and architecture decision-making opportunities.

📝 Enhancement Note: This role offers a clear path for career progression, with the opportunity to grow into a performance architect position and expand technical expertise in AI-GPU hardware architecture.

🌐 Work Environment

Office Type: On-site, with a focus on collaboration and knowledge sharing.

Office Location(s): Leixlip, Ireland (with potential for remote work in the future)

Workspace Context:

Collaborative workspace with access to cutting-edge hardware and software tools.
Multiple monitors and testing devices available to support performance analysis and optimization tasks.
Cross-functional team interaction and collaboration opportunities with hardware, software, and partner teams.

Work Schedule: Full-time (40 hours/week), with flexibility for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: Intel's on-site work environment fosters collaboration and knowledge sharing, with access to cutting-edge hardware and software tools to support GPU performance engineering tasks.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone Screen: Assess problem-solving skills and technical expertise in GPU architecture, AI workloads, and performance analysis.
On-site Technical Interview: Evaluate coding and problem-solving skills, with a focus on performance analysis and optimization techniques.
Behavioral Interview: Assess cultural fit, collaboration skills, and adaptability in a fast-paced hardware/software development environment.
Final Decision: Evaluate overall fit, technical expertise, and alignment with Intel's AI-GPU architecture team goals.

Portfolio Review Tips:

Highlight relevant projects and case studies that demonstrate your ability to identify and resolve performance bottlenecks in GPU-based systems.
Showcase your proficiency in Python, C/C++, and other relevant programming languages through code examples and technical explanations.
Prepare a portfolio that showcases your ability to work effectively in a collaborative, cross-functional team environment.

Technical Challenge Preparation:

Brush up on GPU architecture, AI workloads, and performance analysis techniques.
Practice coding and problem-solving exercises that focus on performance analysis and optimization.
Familiarize yourself with Intel's AI-GPU architecture and performance analysis tools.

ATS Keywords:

GPU Architecture
AI Workloads
Performance Analysis
Performance Optimization
Python
C/C++
Data Analysis
Deep Learning
Large Language Models
Stable Diffusion
Microarchitecture
RTL Design
Memory Design
Collaboration
Adaptability
Analytical Skills

📝 Enhancement Note: A well-prepared portfolio and strong performance in technical interviews will be crucial for success in this role, as they demonstrate your technical expertise and ability to drive GPU performance improvements.

🛠 Technology Stack & Web Infrastructure

Hardware Technologies:

Intel GPU architecture
AI workload-specific hardware (e.g., Tensor Cores, bfloat16 support)

Software Technologies:

Python, C/C++
Performance analysis tools (e.g., Intel VTune, NVIDIA Nsight, etc.)
AI frameworks (e.g., TensorFlow, PyTorch, etc.)
Deep learning libraries (e.g., cuDNN, MKL-DNN, etc.)

Infrastructure Tools:

CI/CD pipelines (e.g., Jenkins, GitLab CI/CD, etc.)
Version control systems (e.g., Git, SVN, etc.)
Collaboration tools (e.g., JIRA, Confluence, etc.)

📝 Enhancement Note: Familiarity with Intel's GPU architecture, AI workload-specific hardware, and relevant software tools will be essential for success in this role.

👥 Team Culture & Values

Web Development Values:

Innovation: Drive hardware and software innovations to shape future Intel GPU design decisions.
Collaboration: Work effectively with cross-functional teams to deliver high-performance solutions.
Performance: Identify and resolve performance bottlenecks to optimize GPU performance.
Adaptability: Thrive in a fast-paced hardware/software development environment and adapt to new technologies and workloads.

Collaboration Style:

Cross-functional Integration: Collaborate with hardware, software, and partner teams to deliver high-performance solutions.
Code Review Culture: Participate in regular code reviews to ensure code quality and performance optimization.
Knowledge Sharing: Contribute to a culture of learning and continuous improvement by sharing expertise and best practices with team members.

📝 Enhancement Note: Intel's culture values innovation, collaboration, and performance, with a focus on driving hardware and software innovations to shape future GPU design decisions.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Workload Analysis: Develop a deep understanding of AI workloads and their impact on GPU performance.
Performance Optimization: Identify and resolve performance bottlenecks in GPU-based systems.
Tool Development: Develop and enhance internal performance analysis tools to streamline debugging and optimization processes.
Emerging Technologies: Stay up-to-date with the latest AI workloads, GPU architecture, and performance analysis techniques.

Learning & Development Opportunities:

Technical Skill Development: Expand expertise in AI workloads, GPU architecture, and performance analysis techniques.
Leadership Development: Develop leadership skills through mentoring, team management, and architecture decision-making opportunities.
Conference Attendance: Attend industry conferences and events to stay up-to-date with the latest trends and best practices in GPU performance engineering.

📝 Enhancement Note: This role offers a range of technical challenges and learning opportunities, with the potential for significant growth and impact on Intel's AI-GPU architecture and performance.

💡 Interview Preparation

Technical Questions:

GPU Architecture: Explain the architecture of Intel's AI-GPU hardware and its impact on performance.
AI Workloads: Describe the characteristics of AI workloads and their impact on GPU performance.
Performance Analysis: Walkthrough a performance analysis process, including data collection, analysis, and optimization strategies.
Tool Development: Discuss your experience with performance analysis tools and their role in driving GPU performance improvements.

Company & Culture Questions:

Intel's AI-GPU Architecture: Explain how Intel's AI-GPU architecture influences the product roadmap and drives performance improvements.
Collaboration: Describe your experience working with cross-functional teams and driving performance improvements through collaboration.
Adaptability: Discuss your ability to thrive in a fast-paced hardware/software development environment and adapt to new technologies and workloads.

Portfolio Presentation Strategy:

Live Demonstration: Prepare a live demonstration of your performance analysis and optimization techniques, using relevant tools and datasets.
Code Explanation: Be prepared to explain your code and technical approach to performance analysis and optimization.
User Experience: Highlight the user experience benefits of your performance improvements and their impact on AI workloads.

📝 Enhancement Note: A well-prepared portfolio and strong performance in technical interviews will be crucial for success in this role, as they demonstrate your technical expertise and ability to drive GPU performance improvements.

📌 Application Steps

To apply for this GPU compute performance engineer position at Intel:

Submit Your Application: Visit the Intel careers website and search for the job title "GPU Compute Performance Engineer" to submit your application.
Customize Your Portfolio: Tailor your portfolio to highlight relevant projects and case studies that demonstrate your ability to identify and resolve performance bottlenecks in GPU-based systems.
Optimize Your Resume: Highlight your technical skills and experience with GPU architecture, AI workloads, and performance analysis techniques.
Prepare for Technical Interviews: Brush up on GPU architecture, AI workloads, and performance analysis techniques, and practice coding and problem-solving exercises that focus on performance analysis and optimization.
Research Intel: Familiarize yourself with Intel's AI-GPU architecture, performance analysis tools, and company culture to ensure a strong fit and alignment with the team's goals.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

GPU compute performance engineer