CUDA Engineering Expert
Position:

CUDA Engineering Expert
Type:

Hourly Contract
Compensation:

$80–$100/hour
Location:

Remote
About the Opportunity
This opportunity is for experienced

GPU Performance Engineers, CUDA Developers, and GPU Kernel Optimization Specialists

interested in contributing to advanced AI research and evaluation projects.
The role focuses on analyzing, optimizing, and evaluating GPU kernels across modern hardware architectures. You'll leverage your expertise in

CUDA, C++17, GPU profiling, and performance optimization

to improve computational efficiency and help advance next-generation AI systems.
This is a contract-based opportunity for professionals passionate about maximizing GPU performance and hardware utilization.
Responsibilities
Analyze and optimize GPU kernels for

performance, efficiency, scalability, and hardware utilization .
Use profiler metrics such as

L2 cache hit rate, occupancy, memory throughput, warp efficiency, and related performance indicators

to guide optimization decisions.
Identify bottlenecks in GPU kernel implementations and recommend performance improvements.
Develop, review, and optimize

C++17, Python, and GPU programming code .
Apply expertise in

CUDA, HIP, shader programming, or related GPU programming frameworks

to improve kernel performance.
Document optimization methodologies, profiling results, and engineering decisions with clear technical reasoning.
Collaborate with engineering teams to evaluate and improve AI-related GPU workloads.




Required Qualifications
Availability to work

at least 20 hours per week .
Strong proficiency in

C++ (through C++17) .
Working knowledge of

Python

and

Git .
Professional experience with at least one GPU programming framework, including

CUDA, HIP, Slang, HLSL, GLSL, or similar technologies .
1+ year of skilled or graduate-level research experience

working with GPU programming or optimization.
Strong understanding of GPU architecture and performance profiling techniques.
Experience using profiler metrics to optimize GPU kernels efficiently.
Strong analytical and problem-solving skills with excellent attention to performance optimization.
Preferred Qualifications
Experience with

CUDA C++ Core Libraries ,

inline PTX assembly , or

Tensor Core optimization .
Experience optimizing kernels for

NVIDIA Blackwell

or other modern GPU architectures.
Familiarity with

NVIDIA Nsight Compute

or similar GPU profiling tools.
Experience working with GPU platforms from

NVIDIA, AMD, Qualcomm , or related hardware vendors.
Contributions to open-source GPU optimization or high-performance computing projects.
Experience supporting AI, machine learning, or high-performance computing workloads.
Compensation
Competitive compensation of $80–$100/hour.
Weekly payments.
Independent contractor engagement.
Application Process
Easy Apply on LinkedIn
Check Email for Next Steps
Participate in Resume Evaluation & Interview Stage

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