GPU Software Engineer - AI Acceleration
Abu Dhabi Emirate, UAE/Dubai
Listed on 2026-09-14
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Software Development
AI Engineer (Applied/Software), Software Engineer, C++ Developer, Computer Software / Middleware
GPU Software Engineer - AI Acceleration Job Snapshot Role: GPU Software Engineer - AI Acceleration
Location:
Abu Dhabi Emirate, United Arab Emirates Industry: Computer Software Function: IT-Software Development
Experience:
Strong experience in GPU programming, C , and performance optimization
Job Type: Contractor
Job Details Country:
United Arab Emirates City:
Abu Dhabi Emirate Industry: Computer Software Function: IT-Software Development Salary:
Estimated salary range based on similar jobs in the job city; please confirm the final offer with the employer.
Gender: Any Candidate Nationality:
Any Job Type: Contractor
Role Context The GPU Software Engineer will work at the performance-critical layer between software workloads and modern graphics processing hardware. The assignment requires translating AI and LLM computational requirements into efficient GPU implementations while carefully managing kernel execution, host-device interaction, memory behavior, and hardware utilization. Strong low-level engineering judgment will help ensure workloads achieve measurable improvements in throughput, latency, and computational efficiency.
Key Responsibilities- Design and implement GPU-accelerated software for AI, LLM, and compute-intensive workloads.
- Develop high-performance GPU functionality using CUDA, WebGPU, GLSL, or the most appropriate technology for each task.
- Profile GPU kernels and shaders to identify execution, memory, synchronization, and resource-utilization bottlenecks.
- Optimize kernels for improved throughput, latency, computational efficiency, and hardware utilization.
- Develop robust host-side C logic and integrate CPU-side applications with GPU workloads.
- Create technically rigorous GPU-focused tasks and reference solutions for AI and large language model applications.
- Analyze runtime behavior and use performance evidence to determine appropriate optimization strategies.
- Refactor performance-sensitive GPU and C code where architectural or implementation changes can improve efficiency.
- Evaluate memory access patterns, data movement, thread execution, and workload organization when diagnosing performance constraints.
- Optimize shader implementations used for graphics or general-purpose GPU computation.
- Benchmark revised implementations to confirm that optimization work produces measurable improvements.
- Apply GPU architecture knowledge when adapting workloads to NVIDIA hardware and other supported execution environments.
- Document implementation decisions, performance findings, optimization techniques, and technical outcomes clearly.
- Strong professional experience developing software for GPUs, particularly NVIDIA GPU platforms.
- Advanced C programming ability with experience building performance-sensitive applications.
- Practical proficiency in CUDA, WebGPU, GLSL, or a combination of these technologies.
- Detailed understanding of GPU architecture, parallel execution, memory hierarchy, and performance characteristics.
- Experience profiling and optimizing GPU kernels, compute workloads, or graphics shaders.
- Able to diagnose bottlenecks systematically and translate profiling findings into effective code-level improvements.
- Background in machine learning acceleration, graphics programming, scientific computing, high-performance computing, or another GPU-intensive discipline.
- Familiarity with host-device programming models and efficient CPU-GPU integration.
- Strong understanding of computational efficiency, memory optimization, parallelism, and workload scheduling.
- Comfortable tackling technically complex problems independently in a remote contractor environment.
- Able to communicate optimization strategies and engineering trade-offs precisely.
- GPU programming
- NVIDIA GPUs
- CUDA
- CUDA C
- C
- WebGPU
- GLSL
- GPU kernel development
- GPU kernel optimization
- Shader programming
- Compute shaders
- GPU performance profiling
- GPU architecture
- Parallel computing
- High-performance computing
- AI acceleration
- LLM acceleration
- Machine learning acceleration
- Scientific computing
- Graphics programming
- Host-device integration
- Memory optimization
- Performance benchmarking
- Bottleneck analysis
- GPU workload optimization
This contractor role provides direct exposure to advanced GPU engineering for AI and large language model workloads, an area where specialized performance expertise continues to be highly valuable. The work combines low-level C , CUDA, shaders, parallel computing, and hardware-aware optimization rather than limiting the engineer to conventional application development. The…
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