📍 Job Overview

Job Title: Senior HPC Performance Engineer
Company: NVIDIA
Location: Germany, Remote
Job Type: Full-Time
Category: DevOps Engineer, System Administrator, Web Infrastructure
Date Posted: 2025-07-29
Experience Level: 5-10 years
Remote Status: Remote

🚀 Role Summary

Key Responsibilities: Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters. Collaborate with a dynamic team across multiple time zones.
Key Skills: HPC, Performance Engineering, Parallel Programming, Communication Runtime, Performance Benchmarking, Computer System Architecture, HW-SW Interactions, Operating Systems Principles, C/C++, Python, Containers, Cloud Provisioning, Kubernetes, SLURM, Ansible, Docker.

📝 Enhancement Note: This role focuses on optimizing performance in large-scale, high-performance computing (HPC) environments, requiring a strong background in performance engineering, parallel programming, and system architecture.

💻 Primary Responsibilities

Performance Analysis: Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters to identify bottlenecks and optimize performance.
HW-SW Interaction: Study the interaction of communication libraries with hardware (GPU, CPU, networking) and software components in the stack to understand performance impact.
Proof-of-Concept Evaluation: Evaluate proof-of-concepts and conduct trade-off analysis when multiple solutions are available to make informed decisions.
Issue Triage: Triage and root-cause performance issues reported by customers to ensure timely resolution and minimal impact on user experience.
Data Collection & Analysis: Collect performance data and build tools and infrastructure to visualize and analyze information, enabling data-driven decision-making.
Team Collaboration: Collaborate with a dynamic team across multiple time zones to ensure effective communication and coordination.

📝 Enhancement Note: The primary responsibilities of this role require a deep understanding of HPC environments, performance engineering, and system architecture to drive performance optimization and improve user experience.

🎓 Skills & Qualifications

Education: A Master's degree (or equivalent experience) or PhD in Computer Science, or a related field, with a strong focus on performance engineering and HPC.

Experience: At least 3 years of experience in parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM).

Required Skills:

Proficiency in C/C++ and Python for implementing micro-benchmarks and debugging performance issues.
Strong understanding of computer system architecture, hardware-software interactions, and operating systems principles.
Experience conducting performance benchmarking and triage on large-scale HPC clusters.
Familiarity with containers, cloud provisioning, and scheduling tools (Kubernetes, SLURM, Ansible, Docker).
Ability to adapt and learn new areas and tools, with a passion for driving innovation.

Preferred Skills:

Practical experience with Infiniband/Ethernet networks, such as RDMA, topologies, and congestion control.
Experience debugging network issues in large-scale deployments.
Familiarity with CUDA programming and/or GPUs.
Experience with Deep Learning Frameworks such as PyTorch or TensorFlow.

📝 Enhancement Note: The required and preferred skills for this role emphasize a strong background in performance engineering, HPC, and system architecture, with a focus on driving innovation and optimizing performance in large-scale environments.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Performance Analysis Projects: Highlight projects demonstrating your ability to conduct in-depth performance characterization and analysis on large-scale HPC clusters.
HW-SW Interaction Case Studies: Showcase case studies illustrating your understanding of hardware-software interactions and their impact on performance.
Issue Triage Demonstrations: Present examples of successfully triaging and resolving performance issues in HPC environments.
Data Visualization & Analysis: Include projects showcasing your ability to collect, analyze, and visualize performance data to drive informed decision-making.

Technical Documentation:

Code Quality & Documentation: Demonstrate your commitment to code quality, commenting, and documentation standards by providing examples of well-documented code.
Version Control & Deployment Processes: Showcase your experience with version control systems, deployment processes, and server configuration to ensure smooth collaboration and efficient workflows.
Testing Methodologies: Present your understanding of testing methodologies, performance metrics, and optimization techniques by including relevant projects and case studies.

📝 Enhancement Note: The portfolio requirements for this role focus on demonstrating your ability to analyze, optimize, and document performance in large-scale HPC environments, with a strong emphasis on hardware-software interactions and data-driven decision-making.

💵 Compensation & Benefits

Salary Range: €80,000 - €120,000 per year (based on industry standards for senior performance engineering roles in Germany, adjusted for remote work and experience level).

Benefits:

Competitive health, dental, and vision insurance plans.
Retirement savings plans with company matching.
Generous time-off policies, including vacation, sick leave, and holidays.
Employee stock purchase plan.
Tuition reimbursement and professional development opportunities.
Fitness reimbursement and wellness programs.
Employee discounts on NVIDIA products and services.

Working Hours: Full-time position with a standard workweek of 40 hours, with flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The salary range and benefits for this role are based on industry standards for senior performance engineering roles in Germany, adjusted for remote work and experience level. The benefits package is designed to attract and retain top talent in the competitive HPC and performance engineering field.

🎯 Team & Company Context

🏢 Company Culture

Industry: NVIDIA operates in the technology industry, focusing on artificial intelligence, high-performance computing, and visualization. This role will work closely with teams developing communication libraries crucial for scaling deep learning and HPC applications.

Company Size: NVIDIA is a large, multinational corporation with a significant global presence, employing over 20,000 people worldwide. This role will be part of a dynamic team working on cutting-edge technologies.

Founded: NVIDIA was founded in 1993 and has since grown to become a leader in the GPU market, with a strong focus on driving innovation in AI, HPC, and visualization.

Team Structure:

Team Size: The team consists of performance engineers, software developers, and hardware engineers working together to optimize communication libraries for large-scale HPC and deep learning applications.
Reporting Structure: This role will report directly to the team lead, with regular interactions with other team members, stakeholders, and cross-functional teams.
Cross-Functional Collaboration: The role will collaborate with various teams, including software development, hardware engineering, and product management, to ensure optimal performance and user experience.

Development Methodology:

Agile/Scrum Methodologies: The team follows Agile/Scrum methodologies for sprint planning, code reviews, and quality assurance.
Code Review & Testing: The team emphasizes code review, testing, and quality assurance practices to ensure high-quality, performant code.
Deployment Strategies: The team employs deployment strategies, CI/CD pipelines, and server management practices to ensure efficient and reliable deployment of communication libraries.

Company Website: https://www.nvidia.com/

📝 Enhancement Note: NVIDIA's company culture emphasizes innovation, collaboration, and driving technological advancements in AI, HPC, and visualization. The team structure and development methodologies foster a dynamic and agile environment for optimizing performance in large-scale HPC and deep learning applications.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level position, focusing on driving performance optimization and innovation in large-scale HPC environments. The role requires a deep understanding of system architecture, hardware-software interactions, and performance engineering.

Reporting Structure: The role reports directly to the team lead, with regular interactions with other team members, stakeholders, and cross-functional teams. This structure enables effective communication and collaboration, driving performance optimization and user experience improvements.

Technical Impact: The role has a significant technical impact on the development and optimization of communication libraries, directly influencing the performance of large-scale HPC and deep learning applications. This impact extends to improving user experience and driving innovation in AI, HPC, and visualization.

Growth Opportunities:

Technical Leadership: With experience and demonstrated expertise, this role can grow into a technical leadership position, guiding the team's performance optimization strategies and driving innovation in HPC and deep learning applications.
Architecture Decisions: As the role gains experience and expertise, it may be called upon to make architecture decisions, influencing the design and development of communication libraries and HPC environments.
Emerging Technology Adoption: The role may have the opportunity to explore and adopt emerging technologies, driving innovation and performance improvements in HPC and deep learning applications.

📝 Enhancement Note: This role offers significant growth opportunities in technical leadership, architecture decisions, and emerging technology adoption, enabling experienced performance engineers to drive innovation and optimize performance in large-scale HPC environments.

🌐 Work Environment

Office Type: NVIDIA's work environment is a hybrid of on-site and remote work, with a focus on fostering collaboration and communication among team members.

Office Location(s): The role is based in Germany, with remote work options available. NVIDIA has offices in various locations worldwide, enabling collaboration with team members across multiple time zones.

Workspace Context:

Collaborative Workspace: NVIDIA's workspace is designed to encourage collaboration, with open-plan offices, meeting spaces, and breakout areas for team discussions and brainstorming sessions.
Development Tools & Infrastructure: The workspace is equipped with state-of-the-art development tools, multiple monitors, and testing devices to ensure optimal performance and user experience.
Cross-Functional Collaboration: The workspace facilitates cross-functional collaboration with designers, marketers, and other stakeholders, ensuring a user-centered approach to performance optimization and innovation.

Work Schedule: The role follows a standard full-time workweek of 40 hours, with flexibility for project deadlines, maintenance windows, and collaboration with team members across multiple time zones.

📝 Enhancement Note: NVIDIA's work environment fosters collaboration, communication, and innovation, with a focus on driving performance optimization and user experience improvements in large-scale HPC and deep learning applications.

📄 Application & Technical Interview Process

Interview Process:

Technical Phone Screen: A 30-45 minute phone or video call to assess your technical skills, with a focus on performance engineering, HPC, and system architecture.
On-Site Technical Deep Dive: A half-day on-site or virtual session focusing on your ability to conduct performance analysis, optimize performance, and make informed decisions based on data.
Behavioral & Cultural Fit Assessment: A discussion to evaluate your problem-solving skills, adaptability, and cultural fit within the NVIDIA team.
Final Evaluation: A final review of your qualifications, technical skills, and cultural fit to determine if you are the best candidate for the role.

Portfolio Review Tips:

Performance Analysis Projects: Highlight projects demonstrating your ability to conduct in-depth performance characterization and analysis on large-scale HPC clusters.
HW-SW Interaction Case Studies: Showcase case studies illustrating your understanding of hardware-software interactions and their impact on performance.
Issue Triage Demonstrations: Present examples of successfully triaging and resolving performance issues in HPC environments.
Data Visualization & Analysis: Include projects showcasing your ability to collect, analyze, and visualize performance data to drive informed decision-making.

Technical Challenge Preparation:

Performance Analysis Challenges: Familiarize yourself with performance analysis tools, techniques, and best practices to ensure you can effectively conduct performance characterization and optimization in large-scale HPC environments.
HW-SW Interaction Challenges: Brush up on your understanding of hardware-software interactions, system architecture, and operating systems principles to ensure you can make informed decisions based on data.
Problem-Solving Challenges: Prepare for problem-solving challenges that may require you to analyze complex performance data, identify bottlenecks, and optimize performance in large-scale HPC environments.

ATS Keywords: (Organized by category)

Programming Languages: C, C++, Python
Web Frameworks: N/A (not applicable for this role)
Server Technologies: NVIDIA GPUs, Infiniband, Ethernet, NVLink, PCIe
Databases: N/A (not applicable for this role)
Tools: Kubernetes, SLURM, Ansible, Docker, CUDA, PyTorch, TensorFlow
Methodologies: Agile, Scrum, Performance Engineering, HPC
Soft Skills: Adaptability, Problem-Solving, Communication, Collaboration
Industry Terms: HPC, Performance Engineering, Parallel Programming, Communication Runtime, Performance Benchmarking, Computer System Architecture, HW-SW Interactions, Operating Systems Principles

📝 Enhancement Note: The interview process for this role focuses on assessing your technical skills in performance engineering, HPC, and system architecture, as well as your problem-solving abilities, adaptability, and cultural fit within the NVIDIA team. The ATS keywords are organized by category to help you optimize your resume and portfolio for this role.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: N/A (not applicable for this role)

Backend & Server Technologies:

Communication Libraries: NCCL, NVSHMEM, GPUDirect (NVIDIA's proprietary communication libraries for HPC and deep learning applications)
Hardware Platforms: NVIDIA GPUs, Infiniband, Ethernet, NVLink, PCIe
Operating Systems: Linux, Windows (depending on the deployment environment)

Development & DevOps Tools:

Version Control: Git, GitHub
CI/CD Pipelines: Jenkins, GitLab CI/CD
Server Management: Ansible, Puppet
Performance Analysis Tools: NVIDIA Nsight, NVIDIA CUDA Profiler, Valgrind, gprof
Data Visualization Tools: Python data visualization libraries (Matplotlib, Seaborn), Tableau, PowerBI

📝 Enhancement Note: The technology stack for this role focuses on NVIDIA's proprietary communication libraries, hardware platforms, and development tools, enabling performance optimization and innovation in large-scale HPC and deep learning applications.

👥 Team Culture & Values

Web Development Values:

Performance Optimization: NVIDIA values performance optimization and drives innovation in AI, HPC, and visualization by continuously improving the performance of communication libraries and HPC environments.
User Experience: NVIDIA prioritizes user experience by ensuring optimal performance and minimal latency in large-scale HPC and deep learning applications.
Innovation: NVIDIA fosters a culture of innovation, encouraging team members to explore new technologies, tools, and approaches to drive performance improvements and user experience enhancements.
Collaboration: NVIDIA emphasizes collaboration, with a focus on effective communication, knowledge sharing, and teamwork to drive performance optimization and innovation in HPC and deep learning applications.

Collaboration Style:

Cross-Functional Integration: NVIDIA encourages cross-functional integration between performance engineers, software developers, hardware engineers, and other stakeholders to ensure optimal performance and user experience.
Code Review Culture: NVIDIA fosters a code review culture, with a focus on peer programming, knowledge sharing, and continuous learning to drive performance improvements and innovation.
Knowledge Sharing & Mentoring: NVIDIA values knowledge sharing and mentoring, with a focus on helping team members grow their skills and advance their careers in performance engineering, HPC, and system architecture.

📝 Enhancement Note: NVIDIA's team culture emphasizes performance optimization, user experience, innovation, and collaboration, with a focus on driving performance improvements and user experience enhancements in large-scale HPC and deep learning applications.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Large-Scale Performance Optimization: Identify and optimize performance bottlenecks in large-scale HPC environments, with a focus on driving innovation and user experience enhancements.
HW-SW Interaction Optimization: Study the interaction of communication libraries with hardware and software components in the stack to understand performance impact and drive optimization.
Emerging Technology Adoption: Explore and adopt emerging technologies, tools, and approaches to drive performance improvements and user experience enhancements in HPC and deep learning applications.
User Experience Enhancement: Continuously improve the user experience by minimizing latency, optimizing performance, and driving innovation in HPC and deep learning applications.

Learning & Development Opportunities:

Performance Engineering Specialization: Deepen your expertise in performance engineering, HPC, and system architecture by attending industry conferences, obtaining certifications, and engaging with online communities.
Emerging Technology Exploration: Explore and learn about emerging technologies, tools, and approaches in performance engineering, HPC, and system architecture to drive innovation and user experience enhancements.
Technical Mentoring & Leadership: Mentor and guide junior team members in performance engineering, HPC, and system architecture, driving their professional growth and development.
Architecture Decision-Making: Participate in architecture decision-making processes, influencing the design and development of communication libraries and HPC environments.

📝 Enhancement Note: The technical challenges and growth opportunities for this role focus on driving performance optimization, innovation, and user experience enhancements in large-scale HPC and deep learning applications, with a strong emphasis on emerging technology adoption, knowledge sharing, and mentoring.

💡 Interview Preparation

Technical Questions:

Performance Analysis: Describe your experience conducting performance analysis on large-scale HPC environments, and how you identified and optimized performance bottlenecks.
HW-SW Interaction: Explain your understanding of hardware-software interactions, system architecture, and operating systems principles, and how you've applied this knowledge to drive performance optimization in HPC environments.
Problem-Solving: Present a complex performance engineering challenge you've faced, and walk through your approach to identifying, analyzing, and optimizing performance bottlenecks in a large-scale HPC environment.

Company & Culture Questions:

NVIDIA's Performance Engineering Culture: Describe what you understand about NVIDIA's performance engineering culture, and how you believe you can contribute to driving innovation and user experience enhancements in HPC and deep learning applications.
Cross-Functional Collaboration: Explain your experience working with cross-functional teams, and how you've driven performance optimization and user experience improvements through effective communication, knowledge sharing, and collaboration.
User Experience Impact: Describe how you've measured and improved user experience in HPC environments, and how you plan to drive user experience enhancements at NVIDIA.

Portfolio Presentation Strategy:

Performance Analysis Projects: Highlight projects demonstrating your ability to conduct in-depth performance characterization and analysis on large-scale HPC clusters, with a focus on driving performance optimization and user experience enhancements.
HW-SW Interaction Case Studies: Showcase case studies illustrating your understanding of hardware-software interactions and their impact on performance, with a focus on driving optimization and user experience improvements in HPC environments.
Issue Triage Demonstrations: Present examples of successfully triaging and resolving performance issues in HPC environments, with a focus on driving performance optimization and user experience enhancements.
Data Visualization & Analysis: Include projects showcasing your ability to collect, analyze, and visualize performance data to drive informed decision-making, with a focus on driving performance optimization and user experience improvements in HPC environments.

📝 Enhancement Note: The interview preparation for this role focuses on assessing your technical skills in performance engineering, HPC, and system architecture, as well as your problem-solving abilities, adaptability, and cultural fit within the NVIDIA team. The portfolio presentation strategy emphasizes driving performance optimization and user experience enhancements in large-scale HPC and deep learning applications.

📌 Application Steps

To apply for the Senior HPC Performance Engineer position at NVIDIA:

Tailor Your Resume: Highlight your experience in performance engineering, HPC, and system architecture, with a focus on driving performance optimization and user experience enhancements in large-scale HPC environments.
Prepare Your Portfolio: Showcase your ability to conduct in-depth performance characterization and analysis, optimize performance bottlenecks, and drive user experience enhancements in HPC environments.
Research NVIDIA: Familiarize yourself with NVIDIA's performance engineering culture, cross-functional collaboration, and user experience focus to ensure a strong cultural fit and effective communication during the interview process.
Practice Technical Challenges: Brush up on your performance analysis, hardware-software interaction, and problem-solving skills to ensure you can effectively tackle technical challenges during the interview process.

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

Senior HPC Performance Engineer