CUDA Engineer - Kernel Optimization

Obsidian; Mumbai; On-site; INR 2,066,000 - 4,133,000; Full time

Job summary

Mercor is seeking GPU kernel optimization specialists on a freelance basis to analyze and optimize kernel performance across modern hardware. You will work with C++17, Python, and GPU programming concepts to squeeze efficiency using profiler-guided analysis.

Ideal candidates have 1+ year GPUs experience, strong C++ skills, and familiarity with CUDA or HIP, NSight Compute, and shader programming to drive performance gains across NVIDIA, AMD, or comparable GPUs.

Qualifications

• Fluent in core C++ features through C++17 or later.
• 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 experience (professional or academic).
• Strong understanding of GPU profiler metrics to optimize kernels.
• Ability to optimize kernels without deep context on every algorithm.

Responsibilities

• Analyze and optimize GPU kernels for performance and hardware utilization.
• Use profiler metrics (L2 cache, occupancy, throughput) to guide improvements.
• Review kernel implementations and identify bottlenecks without deep algorithm context.
• Write/modify Reason about C++17, Python, and GPU code.
• Apply CUDA, HIP, shader programming to improve performance outcomes.
• Document optimization decisions clearly, including profiler metric relevance.

Skills

C++

Python

GPU programming

Profiler

CUDA

HIP

Kernel optimization

Tools

NSight Compute

CUDA Toolkit

HIP

Inline PTX

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