A leading energy-sector company is looking for an experienced GPU Software Engineer with strong expertise in CUDA, GPU-accelerated numerical computation, and matrix operations.
The project focuses less on LLM/AI topics and instead centers on power flow calculations and large-scale numerical simulations that must be efficiently executed on NVIDIA GPUs.
You will work on porting, optimizing, and accelerating computational code onto CUDA, leveraging frameworks such as cuBLAS, cuSOLVER, cuSPARSE, or similar, as well as NVIDIA tooling (incl. QDSS, Jetson toolchain if relevant).
Porting and optimizing power flow / power system calculations to run on NVIDIA GPU hardware
Designing and implementing high-performance CUDA kernels for matrix operations and numerical solvers
Profiling and optimizing GPU execution using NVIDIA tooling (e.g., qdss, Nsight Systems/Compute)
Working with large-scale matrix algebra, linear equation solving, iterative solvers, and sparse/dense matrix handling
Adapting existing CPU-based simulation code to GPU environments
Ensuring numerical stability and precision in GPU-accelerated computation
Close collaboration with power system engineers and simulation experts
Documentation and handover of GPU-optimized modules
Optional: contribution to Jetson-based environments if needed
Strong experience in CUDA development (custom kernels, memory management, warp optimization)
Background in numerical linear algebra, matrix operations, and solving systems of equations
Experience with GPU-accelerated libraries such as:
cuBLAS, cuSOLVER, cuSPARSE, Thrust, or similar
Knowledge of NVIDIA debugging/profiling tools (e.g., qdss, Nsight)
Solid understanding of HPC concepts (parallelization, compute efficiency, memory hierarchy)
Ability to work independently in a nearshoring/remote setup
Very good English communication skills
Experience with power flow calculations, electrical grid simulation, or energy modeling
Experience with NVIDIA Jetson platforms
Familiarity with Python bindings (Numba/cuPy) or C++ integration
Background in energy sector or critical infrastructure
Knowledge of GPU cluster environments
Start: Flexible, ideally soon
Duration: 6+ months with likely extension
Mode: Remote / nearshore-friendly
Onsite: Not required regularly
Language: English