Remote | GPU Programming Software Engineer — $60–$95/hour

We are sharing a specialised consulting opportunity for experienced GPU Programming Software Engineers with strong expertise in CUDA, WebGPU, GLSL, C++, GPU architecture, kernel and shader optimisation, and high-performance parallel computing to contribute to an advanced AI training and GPU-programming evaluation project.

Selected professionals will design and implement GPU-focused technical tasks used to train advanced AI systems, with particular emphasis on performance, architecture, kernel optimisation, shader development, and host-side integration. The work is suited to engineers with practical GPU-programming experience across graphics, machine-learning acceleration, scientific computing, high-performance computing, or comparable GPU-intensive domains.

Key Responsibilities

GPU Software Development

• Design and implement GPU-based software solutions
• Develop workloads using CUDA, WebGPU, GLSL, or comparable GPU technologies
• Translate computational requirements into efficient GPU implementations
• Apply appropriate parallelisation strategies to technical problems
• Develop reliable solutions suitable for performance-focused evaluation

CUDA & NVIDIA GPU Programming

• Develop GPU workloads targeting NVIDIA hardware
• Implement and optimise CUDA kernels where applicable
• Evaluate thread organisation, memory usage, and execution behaviour
• Identify performance bottlenecks in GPU workloads
• Apply practical knowledge of NVIDIA GPU execution models

Kernel Optimisation

• Profile GPU kernels to identify computational bottlenecks
• Improve memory-access patterns and execution efficiency
• Evaluate opportunities to reduce latency or increase throughput
• Analyse occupancy, synchronisation, and resource utilisation where relevant
• Validate that performance improvements preserve correctness

Shader & WebGPU Development

• Develop and evaluate GLSL shaders
• Work with WebGPU-based computational or graphics workloads
• Analyse shader performance and implementation quality
• Identify inefficient or incorrect GPU execution patterns
• Optimise shader logic for performance and maintainability

GPU Performance Profiling

• Profile GPU applications and workloads
• Analyse computational and memory-performance characteristics
• Compare alternative GPU implementations
• Identify architecture-specific optimisation opportunities
• Document performance findings and technical trade-offs clearly

C++ Host-Side Development

• Develop host-side logic and integrations in C++
• Manage communication between CPU and GPU components
• Structure GPU workloads within maintainable application code
• Review data-transfer and execution workflows
• Integrate GPU functionality into broader software systems

AI Training Task Development

• Design technically rigorous GPU-programming tasks for AI training
• Create problems that test GPU architecture and performance reasoning
• Develop clear expected outcomes and evaluation criteria
• Review AI-generated GPU solutions for technical correctness
• Provide expert feedback supporting improvement of model performance

Technical Evaluation & Quality Review

• Assess GPU code for correctness, efficiency, and scalability
• Identify architecture, implementation, or performance issues
• Compare alternative technical approaches
• Provide structured explanations of weaknesses and improvements
• Maintain strong engineering-quality standards across project tasks

Ideal Profile

• Advanced professional experience with GPU programming
• Strong proficiency with CUDA, WebGPU, GLSL, or another GPU-programming technology capable of targeting NVIDIA hardware
• Strong C++ proficiency
• Deep understanding of GPU architecture and parallel execution
• Experience profiling and optimising GPU kernels or shaders
• Strong performance-engineering and debugging skills
• Background in graphics programming, machine-learning acceleration, scientific computing, high-performance computing, or another GPU-intensive domain
• Experience analysing memory-access patterns and GPU resource utilisation
• Ability to reason about performance and architectural trade-offs
• Strong technical problem-solving skills
• Ability to communicate complex GPU-programming concepts clearly
• Experience creating or reviewing technically rigorous programming problems is advantageous
• No prior AI-training or model-evaluation experience is required

Engagement Details

• Independent contractor engagement
• Fully remote
• Displayed compensation range: $60–$95/hour
• Actual compensation structure is output-based, with payment made per task that meets project specifications
• Task completion time may vary depending on individual experience and workflow
• Minimum weekly submission requirements apply; the source does not specify the exact number of required tasks
• Work will involve CUDA, WebGPU, GLSL, C++, GPU kernels, shaders, performance profiling, optimisation, and GPU-focused AI training tasks
• Strong hands-on GPU-programming and performance-engineering experience is central to this engagement
• Roles are typically filled within approximately 48 hours
• Selected experts are expected to begin initial tasks within approximately 24–48 hours after onboarding
• Project scope, workload, GPU technologies, and evaluation standards may evolve depending on project requirements
• Work must be completed without using confidential or proprietary information belonging to any employer, client, research institution, software organisation, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy