C++ CUDA Engineers | $100/hr

About the role

You will analyze, profile, and optimize GPU kernels across modern hardware environments using C++17, Python, CUDA, HIP, shaders, and related GPU programming technologies.

What You'll Do

• Analyze and optimize GPU kernels for speed, efficiency, memory usage, and hardware utilization
• Identify performance bottlenecks using profiler-guided analysis
• Use metrics such as occupancy, L2 cache hit rate, L2 throughput, memory bandwidth, latency, and compute utilization
• Review and improve existing C++, Python, CUDA, HIP, shader, or kernel implementations
• Optimize kernels without requiring deep prior knowledge of every underlying algorithm
• Clearly document optimization decisions and performance tradeoffs

Who Can Apply

Relevant backgrounds include: GPU Kernel Engineers, GPU Software Engineers, CUDA Developers, CUDA Engineers, GPU Performance Engineers, GPU Computing Engineers, HPC Engineers, High-Performance Computing Developers, Parallel Computing Engineers, Graphics Engineers, Rendering Engineers, Shader Engineers, GPGPU Engineers, Compiler Engineers, Performance Optimization Engineers, Systems Performance Engineers, Machine Learning Systems Engineers, AI Infrastructure Engineers, Deep Learning Performance Engineers, ML Compiler Engineers, Inference Optimization Engineers, Training Optimization Engineers, Embedded GPU Engineers, Scientific Computing Engineers, and Research Engineers.

Professionals with experience in CUDA, HIP, ROCm, Slang, HLSL, GLSL, OpenCL, SYCL, Vulkan Compute, Metal Shading Language, Triton, PTX, tensor cores, GPU compilers, parallel programming, or heterogeneous computing are encouraged to apply.

Requirements

• Strong command of C++ through C++17
• Working knowledge of Python and Git
• Fluency in at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, OpenCL, SYCL, or a related technology
• At least 1 year of professional or graduate-level GPU programming or research experience
• Experience profiling and optimizing GPU kernels
• Understanding of GPU architecture, memory hierarchy, occupancy, parallelism, and performance metrics

Preferred Experience

• CUDA C++ Core Libraries
• Inline PTX assembly
• Tensor core-level optimization
• NVIDIA Blackwell, Hopper, or Ampere architectures
• AMD GPU or ROCm optimization
• NVIDIA Nsight Compute or similar GPU profiling tools
• Triton kernel development
• GPU compiler or ML compiler optimization
• Open-source GPU kernel contributions
• Previous experience with NVIDIA, AMD, Qualcomm, Intel, or another GPU hardware organization

This opportunity is ideal for specialists who enjoy profiling low-level code, identifying hardware bottlenecks, and extracting maximum performance from modern GPU architectures.