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Developer Technology Engineer – Energy

NVIDIA

New
🇬🇧 English
C/C++ Python Linux CUDA Nsight Systems Nsight Compute MPI NCCL cuBLAS cuFFT cuSPARSE cuSOLVER

Job description

About the role

NVIDIA is seeking a world‑class computer scientist to accelerate energy‑related simulation and AI workflows on NVIDIA GPUs. The Developer Technology Engineer will focus on CUDA performance optimization for workloads such as seismic processing, reservoir simulation, and power‑grid modeling, collaborating directly with customers, partners and internal product teams.

Key responsibilities

  • Profile, analyze and optimize GPU‑accelerated applications, emphasizing CUDA kernels, memory movement, concurrency and end‑to‑end throughput.
  • Drive performance improvements across the stack, including kernel tuning, launch configuration, memory hierarchy and stream/event usage.
  • Scale applications on multi‑GPU and multi‑node systems using MPI, NCCL and CPU/GPU overlap techniques.
  • Build reproducible benchmarks, performance reports and tuning recommendations.
  • Develop reference implementations, examples or patches to enable performance and portability for customers.
  • 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 software development and performance engineering.
  • Strong communication skills, able to convey technical findings to engineers and non‑engineers.

Required skills

  • Proficient in C/C++ and Python on Linux.
  • Hands‑on experience with CUDA programming and GPU performance optimization.
  • Familiarity with profiling tools such as NVIDIA Nsight Systems and Nsight Compute.
  • Understanding of parallel computing concepts including MPI, NCCL, vectorization, threading and memory hierarchy.
  • Experience with GPU libraries such as cuBLAS, cuFFT, cuSPARSE and cuSOLVER.

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Published 1 day ago

Expires 1 month from now

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