At a Glance
- Tasks: Analyse and optimise large-scale AI workloads for peak performance and efficiency.
- Company: Join NVIDIA, a leader in AI and computer graphics innovation.
- Benefits: Competitive salary, cutting-edge technology, and opportunities for professional growth.
- Other info: Collaborate with experts and drive advancements in AI technology.
- Why this job: Shape the future of AI with impactful work in a dynamic team.
- Qualifications: 12+ years in performance engineering with strong C++ and Python skills.
The predicted salary is between 63000 - 77000 £ per year.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Joining NVIDIA's DGX Cloud AI Efficiency Team means advancing the performance, efficiency, and resiliency of large-scale AI workloads. We help AI researchers and platform teams understand end-to-end behaviour across GPUs, networking, storage, and software stacks.
We are seeking a Performance Engineer to characterize workloads, establish performance baselines, diagnose bottlenecks, and drive optimizations from investigation through deployment. Your work will shape scalable DGX Cloud systems, turn complex measurements into prioritized engineering decisions, and continuously raise the performance and reliability of AI workloads. Join our technically diverse team of infrastructure experts to unlock more efficient AI at scale.
What you'll be doing:
- Analyse end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.
- Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
- Define performance and efficiency evaluation methodologies, benchmarks, and success metrics for AI workloads.
- Use profiling, observability, and data analysis to turn performance measurements into actionable optimization plans.
- Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver performance improvements.
- Communicate performance findings, tradeoffs, and recommendations clearly to influence system and software design decisions.
What we need to see:
- BS or higher degree in computer science, computer engineering, or a related field, with 12+ years of experience.
- Strong programming skills in C++ and Python, with the ability to build reliable analysis and automation workflows.
- Solid foundation in operating systems, computer architecture, and distributed systems.
- Experience with performance engineering, benchmarking, profiling, and optimization of complex software or systems.
- Ability to communicate technical findings, prioritize high-impact work, and build alignment across teams.
Ways to stand out from the crowd:
- Experience analyzing large-scale AI clusters or distributed training and inference workloads.
- Experience with CUDA, GPU computing systems, and GPU performance analysis.
- Hands-on experience with deep learning frameworks such as PyTorch or JAX/XLA.
- Deep understanding of system-level performance analysis, workload characterization, and optimization.
NVIDIA leads the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions, from artificial intelligence to autonomous cars. NVIDIA is looking for exceptional people like you to help us accelerate the next wave of artificial intelligence.
Senior Performance Engineer employer: Nvidia
NVIDIA is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration among talented professionals. With a focus on cutting-edge technology in AI and cloud systems, employees benefit from competitive salary packages and ample opportunities for personal and professional growth in a dynamic environment. Join us to be part of a team that is not only solving significant challenges but also shaping the future of technology.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Performance Engineer
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Nvidia or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Nvidia.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Nvidia.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Nvidia that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Senior Performance Engineer
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Nvidia.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Nvidia and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Nvidia
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Nvidia uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
