At a Glance
- Tasks: Optimise GPU performance for energy simulations and AI workflows using cutting-edge technology.
- Company: Join NVIDIA, a leader in visual computing and AI innovation.
- Benefits: Competitive salary, health benefits, remote work options, and career development opportunities.
- Other info: Dynamic team environment with excellent growth potential and creative freedom.
- Why this job: Make a real impact in energy simulation while working with the latest tech.
- Qualifications: Strong programming skills in C/C++ and Python; CUDA experience preferred.
The predicted salary is between 60000 - 80000 £ per year.
Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team!
NVIDIA is looking for a passionate, world-class computer scientist and engineer (Compute Developer Technology - DevTech) to accelerate Energy simulation and AI workflows on NVIDIA platforms. You will focus on CUDA performance optimization for workloads such as seismic processing (e.g., imaging/inversion pipelines), reservoir simulation, power grid simulators, and related HPC/AI production workflows. You will work hands-on with customer and partner engineering teams as well as NVIDIA product and engineering groups to deliver measurable speedups and scalable performance on multi-GPU and multi-node systems.
Responsibilities
- Profile, analyze, and optimize GPU-accelerated applications with emphasis on CUDA kernels, memory movement, concurrency, and end-to-end throughput.
- Drive performance improvements across the stack:
- CUDA C++ kernel optimization, launch configuration, memory hierarchy, streams/events
- GPU libraries (as applicable): cuBLAS, cuFFT, cuSPARSE, cuSOLVER, NCCL
- Multi-GPU and multi-node scaling using MPI + NCCL, CPU/GPU overlap, communication patterns
- Build reproducible benchmarks, performance reports, and tuning recommendations (before/after, methodology, scaling curves).
- Develop and maintain reference implementations, examples, and/or patches to customer code to enable performance and portability.
- Support customer engagements (POCs to production), including debugging correctness/performance issues and advising on best practices for deployment (containers, schedulers, clusters).
- Collaborate with internal teams to file actionable issues, validate fixes, and influence roadmap based on real customer requirements in Energy.
- Build internal libraries and reusable code that would lead to future NVIDIA products.
What We Need To See
- BS/MS (or equivalent experience) in CS/CE/EE/Physics/Applied Math or related field.
- Strong programming skills in C/C++ and Python on Linux.
- Hands-on experience with CUDA programming and GPU performance optimization concepts.
- Experience profiling and debugging performance using tools such as NVIDIA Nsight Systems / Nsight Compute (or equivalent).
- Understanding of parallel computing and performance fundamentals (vectorization, threading, NUMA, memory bandwidth/latency).
- Ability to communicate technical findings clearly to both engineers and non-engineers.
- 5+ years relevant experience in GPU/HPC optimization; strong track record of delivered speedups and scaling improvements.
Ways To Stand Out From The Crowd
- Leads performance reviews with customer stakeholders; creates reusable playbooks/reference designs.
- HPC experience with MPI, distributed systems, and multi-node performance tuning.
- Energy/HPC domain exposure:
- Seismic processing pipelines, RTM/FWI-style patterns, FFT/stencil/linear algebra heavy codes
- Reservoir simulation (sparse/iterative solvers), preconditioning, domain decomposition
- Power grid simulation / transient stability / optimization workflows
- Experience with CI/perf regression testing, containerized workflows (Docker/Apptainer), and schedulers (Slurm).
- Familiarity with AI workflows used alongside simulation (data prep, training/inference integration, pipeline performance).
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Developer Technology Engineer, Energy employer: NVIDIA AI
NVIDIA is an exceptional employer, renowned for its innovative work in visual and AI computing. With a strong emphasis on employee growth, we offer a collaborative work culture that encourages creativity and autonomy, allowing you to make a meaningful impact in the Energy sector. Our commitment to cutting-edge technology and performance optimization ensures that you will be part of a team that is not only shaping the future of computing but also providing ample opportunities for professional development in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Developer Technology Engineer, Energy
✨Tip Number 1
Network like a pro! Reach out to current NVIDIA employees on LinkedIn or at industry events. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.
✨Tip Number 2
Show off your skills! If you’ve worked on CUDA performance optimisation or similar projects, create a portfolio or GitHub repo showcasing your work. This gives you a chance to demonstrate your expertise beyond just words.
✨Tip Number 3
Prepare for the interview like it’s a big game! Research NVIDIA’s latest projects in energy simulation and AI workflows. Be ready to discuss how your experience aligns with their goals and how you can contribute to their success.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining the NVIDIA team!
We think you need these skills to ace Developer Technology Engineer, Energy
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Developer Technology Engineer role. Highlight your experience with CUDA programming and GPU performance optimisation, as well as any relevant projects that showcase your skills in HPC and AI workflows.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about energy simulation and how your background aligns with NVIDIA's mission. Don't forget to mention specific experiences that demonstrate your problem-solving skills.
Showcase Your Technical Skills:In your application, be sure to highlight your strong programming skills in C/C++ and Python. Mention any hands-on experience you have with profiling tools like NVIDIA Nsight Systems, as this will show us you're ready to hit the ground running.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at NVIDIA AI
✨Know Your CUDA Inside Out
Make sure you brush up on your CUDA programming skills. Be ready to discuss specific optimisations you've implemented in the past, especially around memory movement and concurrency. Having concrete examples will show your hands-on experience and understanding of GPU performance.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've tackled complex performance issues in previous roles. Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on how you identified problems and the measurable improvements you achieved.
✨Familiarise Yourself with NVIDIA Tools
Get comfortable with tools like NVIDIA Nsight Systems and Nsight Compute. Being able to discuss how you've used these tools to profile and debug applications will demonstrate your technical prowess and readiness for the role.
✨Communicate Clearly and Confidently
Practice explaining technical concepts in a way that non-engineers can understand. This is crucial, as you'll need to collaborate with various teams. Clear communication can set you apart from other candidates who may struggle in this area.
