Developer Technology Engineer – Energy Simulations

About the role

NVIDIA is seeking a world‑class Developer Technology Engineer to accelerate energy‑related simulation and AI workflows on NVIDIA GPUs. You will work directly with customers, partners, and internal product teams to deliver measurable performance gains for seismic processing, reservoir simulation, power‑grid modeling, and other high‑performance computing workloads.

Key responsibilities

• Profile, analyze, and optimize GPU‑accelerated applications, focusing on CUDA kernels, memory movement, concurrency, and end‑to‑end throughput.
• Drive performance improvements across the stack, including kernel optimization, launch configuration, memory hierarchy, and stream/event usage.
• Utilize GPU libraries such as cuBLAS, cuFFT, cuSPARSE, cuSOLVER, and NCCL.
• Scale applications across multiple GPUs and nodes using MPI and NCCL, and manage CPU/GPU overlap and communication patterns.
• Build reproducible benchmarks, performance reports, and tuning recommendations.
• Develop reference implementations, examples, or patches to enable performance and portability.
• Support customer engagements from proof‑of‑concept to production, debugging correctness and performance issues.
• Collaborate with internal teams to file actionable issues, validate fixes, and influence product road‑maps.

Required profile

• BS/MS (or equivalent) in Computer Science, Computer Engineering, Electrical Engineering, Physics, Applied Mathematics, or a related field.
• 5+ years of professional experience in GPU performance optimization.
• Strong programming skills in C/C++ and Python on Linux.
• Hands‑on experience with CUDA programming and performance tools such as NVIDIA Nsight Systems or Nsight Compute.
• Solid understanding of parallel computing fundamentals (vectorization, threading, NUMA, memory bandwidth/latency).
• Excellent communication skills for both technical and non‑technical audiences.

Required skills

• CUDA kernel development and optimization.
• C/C++ and Python programming.
• Linux operating system.
• NVIDIA Nsight Systems / Nsight Compute.
• GPU libraries: cuBLAS, cuFFT, cuSPARSE, cuSOLVER, NCCL.
• MPI for multi‑node scaling.