NVIDIA
Energy
DeveloperTechnologyEngineer,Energy
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“Developer Technology Engineer, Energy at NVIDIA. Skills: CUDA, GPU performance optimization, HPC, AI. Profile, analyze, and optimize GPU-accelerated applications. Drive performance improvements across the stack”
What You'll Achieve.
Deliver measurable speedups; Deliver scalable performance
Industry & Context.
Debugging correctness issues
What They're Looking For.
Must Have
BS/MS (or equivalent experience) in CS/CE/EE/Physics/Applied Math or related field, 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 track record of delivered speedups and scaling improvements
Nice to Have
Leads performance reviews with customer 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)
What You'll Do.
and optimize GPU-accelerated applications
Drive performance improvements across the stack
Build reproducible benchmarks
Develop and maintain reference implementations
Support customer engagements
Collaborate with internal teams
Build internal libraries
How You'll Work.
Team & Collaboration
Work hands-on with customer and partner engineering teams; Collaborate with internal teams
Communication Scope
Communicate technical findings clearly
Full Job Description
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
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