Developer Technology Engineer, Energy

Overview

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!
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