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!
Skills Required
- Bachelor’s or master’s degree, or equivalent experience, in computer science, computer engineering, electrical engineering, physics, applied mathematics, or a related field
- Strong programming skills in C or C++ and Python on Linux
- Hands-on CUDA programming experience
- Experience with GPU performance optimization
- Experience profiling and debugging performance using NVIDIA Nsight Systems, Nsight Compute, or equivalent tools
- Understanding of parallel computing and performance fundamentals, including vectorization, threading, NUMA, and memory bandwidth or latency
- Ability to communicate technical findings clearly to engineers and non-engineers
- Five or more years of relevant experience in GPU or HPC optimization
- Experience with MPI, distributed systems, and multi-node performance tuning
- Energy or HPC domain experience in seismic processing, reservoir simulation, or power-grid simulation
- Experience with CI and performance regression testing
- Experience with containerized workflows using Docker or Apptainer
- Experience with schedulers such as Slurm
- Familiarity with AI workflows used alongside simulation
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
