GPU Programming Expert - Fully Remote | Upto $500/task Task based

mercor; United States; Remote; USD 585,000 - 792,000; Part time

Job summary

Mecor is seeking a CUDA Engineering Expert for a remote contract role. You will analyze and optimize GPU kernels, using profiler metrics to improve occupancy, L2 cache hit rates, and overall performance.

Responsibilities include reviewing kernel implementations, writing and modifying C++17, Python, and GPU code, applying CUDA or HIP and shader techniques, and documenting optimization decisions clearly for reproducibility.

Qualifications

• Available to work at least 20 hrs/wk.
• Fluent in core C++ features through C++17.
• Working knowledge of Python and Git.
• Fluent in one GPU programming model like CUDA or HIP.
• 1+ year of professional or research experience with GPUs.
• Strong understanding of GPU profiler performance metrics.

Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
• Use profiler metrics like L2 cache hit rate and occupancy to guide kernel improvements.
• Review GPU kernel implementations and identify bottlenecks without deep algorithmic background.
• Write and modify C++17, Python, and GPU programming code.
• Apply expertise in CUDA, HIP, and shader programming to improve performance.
• Document optimization decisions clearly, focusing on profiler metrics' utility.

Skills

C++17

Python

Git

GPU programming

CUDA

HIP

Nsight Compute

Tools

Nsight Compute

Job description

About the job

Mecor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Job Details

• Position: CUDA Engineering Expert
• Type: Contract
• Compensation: $500/hour
• Location: Remote

Role Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
• Use profiler metrics like L2 cache hit rate and occupancy to guide kernel improvements.
• Review GPU kernel implementations and identify bottlenecks without deep algorithmic background.
• Write and modify C++17, Python, and GPU programming code.
• Apply expertise in CUDA, HIP, and shader programming to improve performance.
• Document optimization decisions clearly, focusing on profiler metrics' utility.

Qualifications - Must-Have

• Available to work at least 20 hrs/wk.
• Fluent in core C++ features through C++17.
• Working knowledge of Python and Git.
• Fluent in one GPU programming model like CUDA or HIP.
• 1+ year of professional or research experience with GPUs.
• Strong understanding of GPU profiler performance metrics.

Preferred

• Experience with CUDA, HIP, and CUDA C++ Core Libraries.
• Experience optimizing kernels for NVIDIA Blackwell hardware.
• Familiarity with NSight Compute.
• Prior experience with NVIDIA, AMD, or Qualcomm.
• Open-source contributions related to GPU kernel optimization.

Originally posted on Himalayas