Developer Technology Engineer – Energy

About the role

NVIDIA is seeking a world‑class computer scientist and engineer to accelerate energy‑focused simulation and AI workflows on NVIDIA platforms. You will work directly with customers, partners, and internal product teams to deliver measurable speedups on multi‑GPU and multi‑node systems.

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 CUDA C++ kernel tuning, launch configuration, memory hierarchy, and stream/event usage.
• Utilize GPU libraries such as cuBLAS, cuFFT, cuSPARSE, cuSOLVER, and NCCL where applicable.
• Scale applications across multiple GPUs and nodes using MPI, NCCL, and CPU/GPU overlap techniques.
• Build reproducible benchmarks, performance reports, and tuning recommendations with clear methodology and scaling curves.
• Develop and maintain reference implementations, examples, or patches to enable performance and portability.
• Support customer engagements from proof‑of‑concept to production, debugging correctness and performance issues and advising on deployment best practices.
• Collaborate with internal teams to file actionable issues, validate fixes, and influence product road‑maps based on real customer requirements.

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 high‑performance computing or GPU‑accelerated software development.
• 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 NVIDIA Nsight Systems, Nsight Compute, or equivalent tools.
• Solid understanding of parallel computing fundamentals (vectorization, threading, NUMA, memory bandwidth/latency).
• Excellent communication skills, able to convey technical findings to both engineers and non‑engineers.

Required skills

• C++
• Python
• Linux
• CUDA programming
• GPU performance optimization
• NVIDIA Nsight Systems
• NVIDIA Nsight Compute
• MPI
• NCCL
• cuBLAS
• cuFFT
• cuSPARSE
• cuSOLVER
• Parallel computing concepts (vectorization, threading, NUMA)
• Memory bandwidth and latency awareness