NVIDIA CUDA Engineer

The NVIDIA CUDA Engineer is responsible for designing and optimizing GPU-accelerated software for high-performance, real-time environments. This role focuses on developing system-level software and parallel computation strategies across domains such as Embedded Systems
, Artificial Intelligence/Machine Learning (AI/ML), and Quantum-Classical Hybrid Computing
.

• GPU Programming: Develop and maintain CUDA-based algorithms
  ; tune GPU kernels for maximum throughput and low-latency.
• Systems Development: Build CUDA runtime libraries, drivers, and toolchain components; manage memory and performance profiling.
• Heterogeneous Strategy: Implement parallel computation strategies across CPU/GPU/FPGA systems.
• Collaboration: Partner with hardware architects to design holistic GPU solutions and accelerate AI workloads.
• Quality Assurance: Enhance Continuous Integration/Continuous Deployment (CI/CD) pipelines and benchmark software performance.

• Expert-level C/C++ programming.
• Deep expertise in NVIDIA CUDA and GPU Architecture.
• Proficiency in Performance Tuning and profiling tools (e.g., Nsight, nvprof).
• Strong grasp of Parallel Programming Paradigms (Multi-threading, Vectorization).
• Experience in System-level Software and Memory Management.

• Experience with Compiler Design (
  LLVM
  , MLIR
  ).
• Knowledge of Quantum-Classical Systems (CUDA-Q).
• Background in Real-time Algorithms and AI Model Acceleration.
• Master’s degree in Computer Science or Electrical/Electronics Engineering.