CUDA Engineer - Kernel Optimization

Obsidian; San Francisco (CA); On-site; USD 165,000 - 276,000; Full time

Job summary

Mercor seeks GPU kernel optimization experts for a freelance project in the San Francisco area. You will analyze, optimize, and reason about GPU kernels across modern hardware, squeezing performance with profiler-guided analysis.

Requirements include strong C++17, Python, and GPU programming experience (CUDA/HIP). Familiarity with NSight Compute and kernel profiling is a plus; work at least 20 hrs/week on contract basis.

Qualifications

• Fluent in core C++ features through C++17.
• Working knowledge of Python and Git.
• Fluent in at least one GPU programming model (CUDA, HIP, Slang, HLSL, GLSL).
• At least 1 year of GPU-related research or professional experience.
• Understanding of GPU profiler metrics and how to optimize kernels.

Responsibilities

• Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
• Use profiler metrics (e.g., L2 cache hit rate, L2 throughput, occupancy) to guide kernel improvements.
• Review kernel implementations and identify bottlenecks with minimal context on 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 outcomes.
• Document optimization decisions clearly, including usefulness of specific profiler metrics.

Skills

C++17

Python

Git

GPU profiling

CUDA

Tools

NSight Compute

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