Developer Technology Engineer, Energy

NVIDIA Gruppe; Zürich; Vor Ort; CHF 140.000 - 200.000; Vollzeit

Zusammenfassung

NVIDIA seeks a compute dev tech focused on accelerating energy simulation and AI workflows. You will optimize CUDA workloads, profile performance, and collaborate with customer and internal teams to deliver speedups on multi-GPU, multi-node systems.

Applicants should have 5+ years in GPU/HPC optimization, strong C/C++ and Python, and experience with Nsight tools. On-site role in Zürich, Switzerland with opportunities to influence NVIDIA’s energy and HPC roadmap.

Qualifikationen

• BS/MS (or equivalent) 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.

Aufgaben

• 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 (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.

Kenntnisse

CUDA programming

C/C++

Python

GPU performance optimization

Nsight Systems

Nsight Compute

MPI

NCCL

memory bandwidth/latency

Ausbildung

BS/MS in CS/CE/EE/Physics/Applied Math or related field

Tools

cuBLAS

cuFFT

cuSPARSE

cuSOLVER

NCCL

MPI

Docker

Slurm

Jobbeschreibung

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 scientists and engineers (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.

What you will be doing:

• 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 resusable 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. Experience/Skills (typical)
• 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!