CUDA / GPU Performance Engineer (Kernel Optimization)
Gramian Consulting Group
$0-$0 / yr
Salary
argentina
Region
ASAP
Start Date
About Gramian Consulting Group
Gramian Consultancy brings together the perspective of a software engineer, the knowledge of a technical recruiter, and the vision of a business builder. This unique experience is our signature advantage to delivering top quality services in the domain of recruiting, staff augmentation, and outsourcing.
About this Role.
About Us
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
Role Overview
We are looking for experienced CUDA and GPU performance engineers to analyze, profile, and optimize high-performance kernels and supporting C++ code. The role combines CUDA optimization, GPU profiling, C++, shader development, and performance analysis across different GPU architectures. No prior AI experience is required; strong systems and GPU engineering expertise is the key requirement.
CONTRACT: Freelance contractor, paid per completed task
COMMITMENT: Flexible, based on available tasks and project demand
LOCATIONS: Fully remote - GLOBAL
PROCESS: Application review, technical assessment, and onboarding
HOURLY RATE: 60-100/h
Responsibilities
Analyze and optimize CUDA kernels for throughput, latency, and hardware utilization.
Profile GPU workloads to identify compute, memory, synchronization, and execution bottlenecks.
Develop and implement targeted kernel optimization strategies.
Refactor C++ and CUDA codebases for performance, maintainability, and portability.
Evaluate kernel behavior across different GPU architectures and hardware generations.
Develop or adapt shader and compute workflows using GLSL and WebGPU.
Use GPU profiling tools to validate improvements and compare performance.
Document optimization approaches, benchmarks, findings, and performance gains.
Contribute technical input to GPU architecture and performance-design discussions.
Evaluate emerging GPU programming techniques and apply relevant improvements.
Requirements
Strong professional experience with CUDA programming and GPU kernel optimization.
Advanced proficiency in C++, ideally in high-performance or systems programming environments.
Proven experience profiling and tuning GPU workloads for performance.
Hands-on experience with GPU profiling tools such as NVIDIA Nsight or comparable tools.
Strong understanding of GPU architecture, memory hierarchy, parallel execution, and synchronization.
Experience analyzing performance across different GPU hardware generations.
Hands-on experience with GLSL and/or WebGPU for shader or compute development.
Ability to document performance findings and technical decisions clearly in English.