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

Obsidian; Mumbai; Remote; INR 2,755,000 - 4,822,000; Part time

Job summary

Mercor is seeking GPU kernel optimization experts to contribute to a project with an AI lab. This contract-based role targets freelancers with strong C++17 skills, practical GPU programming experience, and the ability to optimize kernels using profiler-guided analysis.

You will evaluate, optimize, and reason about GPU kernels across modern hardware, working at least 20 hrs/wk in a freelance capacity.

Qualifications

• Strong C++ skills up to C++17 required.
• Experience profiling GPU kernels and using profiler metrics.
• 1 year of GPU research/industry experience preferred.
• Freelancer able to commit 20 hrs/week.
• Familiarity with CUDA, HIP, or comparable models.

Responsibilities

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

Skills

C++ programming

Python

GPU programming

Profiler-guided optimization

Git

Tools

CUDA

HIP

NSight Compute

inline PTX

Tensor core optimization

Shader programming

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