Remote | GPU Programming Software Engineer — $60–$95/hour
About The Position
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, optimise kernels and shaders, analyse GPU performance, and review AI-generated solutions for correctness and efficiency. The work is suited to engineers with practical GPU-programming experience across graphics, machine-learning acceleration, scientific computing, HPC, or comparable GPU-intensive domains.
Requirements
- Advanced professional experience with GPU programming
- Strong proficiency with CUDA, WebGPU, GLSL, or another GPU 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, machine-learning acceleration, scientific computing, HPC, or another GPU-intensive domain
- Experience analysing memory-access patterns and GPU resource utilisation
- Ability to reason about architecture and performance trade-offs
- Strong technical problem-solving and communication skills
Nice To Haves
- Experience creating or reviewing rigorous programming problems is advantageous
- No prior AI-training or model-evaluation experience is required
Responsibilities
- Design and implement GPU workloads using CUDA, WebGPU, GLSL, or comparable technologies
- Develop and optimise CUDA kernels and shader-based workloads
- Apply appropriate parallelisation strategies and GPU execution models
- Improve memory-access patterns, throughput, latency, occupancy, and resource utilisation
- Ensure performance improvements preserve correctness
- Profile GPU applications to identify computational and memory bottlenecks
- Analyse thread organisation, synchronisation, execution behaviour, and architecture-specific constraints
- Compare alternative GPU implementations and optimisation approaches
- Identify inefficient or incorrect GPU execution patterns
- Document performance findings and technical trade-offs clearly
- Develop host-side logic and integrations in C++
- Manage CPU–GPU communication, data transfer, and execution workflows
- Integrate GPU functionality into broader software systems
- Structure GPU workloads within maintainable application code
- Debug performance and correctness issues across host and device components
- Design technically rigorous GPU-programming tasks for AI training
- Create problems testing GPU architecture, optimisation, and performance reasoning
- Define clear expected outcomes and evaluation criteria
- Review AI-generated GPU solutions for correctness, efficiency, and scalability
- Provide structured technical feedback supporting model improvement
Benefits
- Fully remote
- Output-based compensation structure with payment made per task that meets project specifications
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Career Level
Senior
Education Level
No Education Listed
