CUDA Engineer - Kernel Optimization

Mercor; San Francisco (CA); On-site; USD 96,000 - 165,000; Part time

Job summary

Mercor is seeking GPU kernel optimization experts to contribute to a project with a leading AI lab. This contract-based opportunity is designed for freelancers with strong C++ skills, practical GPU programming experience, and the ability to squeeze performance out of modern GPU architectures.

You will analyze, optimize, and reason about GPU kernels across modern hardware, using profiler-guided analysis. Expect to write C++17 and Python code, apply CUDA or HIP, and document decisions clearly.

Qualifications

• Available to work at least 20 hrs/wk.
• Proficient in C++ through C++17.
• Experience with Python and Git.
• Skilled in GPU kernel programming models (CUDA, HIP, Slang, HLSL, GLSL).
• At least 1 year of GPU experience.
• Strong understanding of GPU profiler metrics.
• Ability to optimize kernels with minimal context.

Responsibilities

• Analyze and optimize GPU kernels for performance and hardware utilization.
• Use profiler metrics (L2 cache, occupancy) to guide improvements.
• Review kernel implementations and identify bottlenecks.
• Write and modify C++17, Python, and GPU code.
• Apply CUDA, HIP, shader programming to improve performance.
• Document optimization decisions clearly.

Skills

C++17

Python

GPU programming

CUDA

HIP

Kernel optimization

Profiling tools

Git

Shader programming

Tools

NSight Compute

CUDA

HIP

PTX

Job description

1. Role Overview

Mercor is seeking GPU kernel optimization experts to contribute to a project with a leading AI lab. This opportunity is designed for freelancers with strong C++ skills, practical GPU programming experience, and the ability to improve kernel performance using profiler-guided analysis. You’ll help evaluate, optimize, and reason about GPU kernels across modern hardware environments. This is a contract-based opportunity for specialists who enjoy squeezing performance out of modern GPU architectures.

2. Key Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization
• Use profiler metrics such as L2 cache hit rate, L2 throughput, occupancy, and related signals to guide kernel improvements
• Review GPU kernel implementations and identify bottlenecks without requiring extensive background in the underlying algorithms
• Write, modify, and reason about C++17, Python, and GPU programming code
• Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance outcomes
• Document optimization decisions clearly, including when specific profiler metrics are or are not useful

3. Ideal 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 at least one GPU programming model, such as CUDA, HIP, Slang, HLSL, GLSL, or related kernel programming
• At least 1 year of professional or graduate-level research experience working with GPUs
• Strong understanding of GPU profiler performance metrics and how to use them to optimize kernels
• Ability to optimize GPU kernels without needing deep prior context on every algorithm
• Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization is a plus
• Experience optimizing kernels for NVIDIA Blackwell hardware is a plus
• Familiarity with NSight Compute is a plus
• Prior experience with GPU hardware organizations such as NVIDIA, AMD, or Qualcomm is a plus
• Open-source contributions related to GPU kernel optimization are a plus