GPU Programming Expert - Fully Remote | Upto $120/hr

mercor; United States; Remote; USD 110,000 - 165,000; Part time

Job summary

Mercor is seeking a CUDA Engineering Expert for a remote contract role. You will analyze and optimize GPU kernels, leveraging CUDA, HIP, and shader programming to maximize performance on modern GPUs.

The role requires 20+ hrs/week, fluent C++17, Python, and Git, plus experience with GPU programming models and profilers. You will collaborate asynchronously with AI research teams and document optimization decisions clearly.

Qualifications

• Proficient with core C++17 features and modern C++ practices.
• Strong Python scripting for tooling and automation.
• Experience with Git for version control and collaboration.
• Fluent with at least one GPU programming model (CUDA, HIP, Slang, HLSL, GLSL).
• 1+ year of GPU-focused research or professional work.
• Solid understanding of GPU profiler metrics for kernel optimization.
• Ability to optimize GPU kernels with limited contextual information.

Responsibilities

• Analyze and optimize GPU kernels for performance and hardware utilization.
• Use profiler metrics (L2 cache, throughput, occupancy) to guide improvements.
• Review kernel implementations to identify bottlenecks without deep algorithm context.
• Write and modify C++17 and Python code for GPU work.
• Apply CUDA, HIP, and shader techniques to boost performance.
• Document optimization decisions and when profiler metrics are helpful.

Skills

C++17 features

Python

Git

GPU programming models

GPU kernel optimization

GPU profiler metrics

Context-free kernel optimization

Tools

NSight Compute

Job description

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: $80–$120/hour

Location: Remote

Role Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
• Use profiler metrics like L2 cache hit rate , L2 throughput , and occupancy to guide kernel improvements.
• Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
• Write, modify, and reason about C++17 , Python , and GPU programming code.
• Apply CUDA , HIP , and shader programming expertise to improve performance outcomes.
• Document optimization decisions clearly, noting when specific profiler metrics are useful.

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 at least one GPU programming model like CUDA , HIP , Slang , HLSL , or GLSL .
• At least 1 year of professional or graduate-level research experience with GPUs .
• Strong understanding of GPU profiler performance metrics for kernel optimization.
• Ability to optimize GPU kernels without deep prior context on every algorithm.

Preferred

• Experience with CUDA , HIP , CUDA C++ Core Libraries , inline PTX assembly, or tensor core-level optimization.
• Experience optimizing kernels for NVIDIA Blackwell hardware .
• Familiarity with NSight Compute .
• Prior experience with GPU hardware organizations like NVIDIA , AMD , or Qualcomm .
• Open-source contributions related to GPU kernel optimization .