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

mercor; United States; Remote; USD 568,000 - 809,000; Full time

Job summary

Mercor is seeking a CUDA Engineering Expert for a remote contract role focused on high-performance GPU kernel optimization. You will analyze kernels, leverage profiling metrics, and drive improvements across CUDA/HIP workflows.

Proficiency in C++17 and Python, plus experience with GPU profiling tools and NVIDIA/AMD ecosystem, will be essential for delivering performance gains remotely.

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 GPU experience.
• Strong understanding of GPU profiler metrics.

Responsibilities

• Analyze and optimize GPU kernels for performance and hardware utilization.
• Use profiler metrics to guide kernel improvements.
• Review GPU kernel implementations and identify bottlenecks.
• Write and modify C++17, Python, and GPU programming code.
• Apply CUDA, HIP, and shader programming to improve performance.
• Document optimization decisions clearly with profiler metrics.

Skills

C++17

Python

Git

CUDA

HIP

GPU profiling

GPU kernels

Tools

CUDA Core Libs

NSight Compute

NVIDIA tools

Job description

About the job Mercor 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 .

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 .