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CUDA/HPC/GPU Engineer
Job in
Waukesha, Waukesha County, Wisconsin, 53188, USA
Listed on 2026-03-11
Listing for:
iTARKS
Full Time
position Listed on 2026-03-11
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Interested candidates pls share your profile with
OverviewAn NVIDIA CUDA Engineer is responsible for designing, developing, optimizing, and maintaining GPU-accelerated software using CUDA, targeting high-performance, real-time, and parallel computing environments. This role is critical in domains like embedded systems, medical devices, automotive, AI/ML, HPC, and quantum-classical hybrid computing.
Key Responsibilities- Develop, optimize, and maintain CUDA-based algorithms for high-performance applications.
- Tune GPU kernels for maximum throughput, efficiency, and low-latency execution.
- Implement parallel and distributed computation strategies for heterogeneous systems (CPU/GPU/FPGA).
- Build and maintain CUDA runtime libraries, drivers, and toolchain components.
- Work on multi-processor system execution, memory management, and performance profiling.
- Develop system-level software supporting NVIDIA GPU hardware.
- Partner with hardware engineers, architects, and product teams to design holistic GPU solutions.
- Work with research teams on advanced real-time algorithms, AI workload acceleration, or CUDA-Q (quantum) frameworks.
- Improve CI/CD pipelines for CUDA components.
- Benchmark, validate, and enhance performance across software releases.
Skills & Qualifications Technical Skills
- Strong C/C++ programming skills (mandatory).
- In-depth experience with CUDA, GPU architecture, performance tuning, and profiling tools.
- Understanding of parallel programming paradigms (multi-threading, vectorization).
- Experience with heterogeneous computing (CPUs, GPUs, FPGAs).
- Exposure to HPC systems.
- Compiler design (LLVM/MLIR).
- Quantum-classical systems (CUDA-Q).
- Distributed systems.
- Real-time algorithms and AI models.
- Bachelor's or Master’s in Computer Science, Electrical, or Electronics Engineering.
- 8+ years in GPU/CUDA software development (varies by role level).
- Experience building robust, scalable production-grade systems.
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