CUDA engineer for AI systems

MWDN is a global IT outstaffing company with 23+ years of experience that connects exceptional tech talent with leading companies across Israel, the USA, Great Britain, and Western Europe. We offer opportunities to work on international products in a stable and professional environment. We’re looking for a highly technical engineer or researcher with deep expertise in GPU computing, CUDA, and LLM inference optimization to work on challenging problems at the intersection of AI systems and high-performance computing. This opportunity is ideal for someone who enjoys understanding how things work under the hood — from GPU architecture and low-level kernels to LLM inference engines and performance bottlenecks.

• Investigate and optimize LLM inference performance on modern GPUs.
• Work with inference and serving frameworks such as vLLM, SGLang, TensorRT-LLM, or similar systems.
• Analyze GPU execution, memory access patterns, kernel performance, and inference bottlenecks.
• Develop, optimize, debug, or reverse-engineer CUDA kernels and low-level GPU operations.
• Explore optimization techniques for matrix multiplication, attention mechanisms, and neural network layers.
• Benchmark performance and identify opportunities to improve latency, throughput, and GPU utilization.
• Work on technically challenging problems requiring strong analytical thinking and hands-on experimentation.

• Strong hands-on experience with CUDA and GPU programming.
• Deep understanding of GPU architecture, memory hierarchy, parallel execution, and performance optimization.
• Experience with LLM inference, AI systems, GPU kernels, or high-performance computing.
• Familiarity with one or more frameworks such as vLLM, SGLang, TensorRT-LLM, Triton, or FlashAttention.
• Experience debugging complex performance issues and investigating low-level system behavior.
• Strong problem-solving skills and the ability to communicate complex technical concepts in English.
• Будет плюсом: PhD or research experience in Computer Science, Machine Learning, Computer Architecture, or a related field; Publications, research projects, or meaningful contributions to open-source GPU/LLM infrastructure; Experience with PyTorch internals, distributed inference, quantization, KV cache optimization, or custom GPU kernels; Contributions to projects such as vLLM, SGLang, Triton, FlashAttention, or related repositories

• People-first management with minimal bureaucracy
• A friendly company culture, proven by employees who choose to return
• Flexible working hours
• 29 days of PTO (18 working days per year pluse all national holidays)
• 10 paid recovery days
• Full financial and legal support for independent contractors
• Free English classes, with native speakers or Ukrainian teachers
• Dedicated HR support