GPU Programming Expert - Fully Remote

Mercor; Mumbai; Remote; INR 1,653,000 - 3,444,000; Full time

Job summary

Mercor seeks GPU kernel optimization experts for a contract-based project with a leading AI lab. The role targets freelancers with strong C++ skills, practical GPU programming experience, and the ability to enhance kernel performance via profiler-guided analysis.

You will analyze and optimize GPU kernels across modern hardware, writing C++17 and Python code, and applying CUDA, HIP, shader programming, or related kernel techniques to achieve better performance.

Qualifications

• Strong C++17 proficiency is required.
• Experience with GPU programming and kernel optimization.
• Familiar with profiler-driven optimization approaches.

Responsibilities

• Analyze and optimize GPU kernels for performance and hardware utilization.
• Use profiler metrics (e.g., L2 cache, occupancy) to guide improvements.
• Review kernel implementations and identify bottlenecks without deep context on each algorithm.
• Write, modify, and reason about C++17 and Python code for GPU tasks.
• Apply CUDA, HIP, or related kernel programming techniques to improve performance.
• Document optimization decisions clearly, noting when metrics are or aren’t useful.

Skills

C++17

Python

GPU programming

Profiling

CUDA

Tools

Git

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