Location: Tel Aviv#HybridDriveNets is a leader in high-scale disaggregated networking solutions. Founded in 2015, DriveNets modernizes the way service providers, cloud providers and hyperscalers build networks. Supporting the largest network in the world, more than half of AT&T’s backbone traffic is running on DriveNets’ Network Cloud open disaggregated architecture. Raising $587 million in three funding rounds, DriveNets is disrupting the networking market from high-scale architecture to AI platforms, and is bringing onboard the most talented people. We are seeking people that want to make an impact on the world’s leading communication networks and are experienced in networking architecture or AI infrastructure solutions. Job SummaryWe are seeking a skilled software engineer to join our NPU software stack development team. This role involves developing high-performance GPU programming frameworks, runtime systems, and libraries for AI/ML workloads. You will be responsible for implementing, optimizing, and maintaining GPU software stack components to support distributed AI training and inference. Key ResponsibilitiesIdentify bottlenecks, analysis and optimize in distributed NPU eco-systemDesign and develop NPU memory management systemDesign and develop optimized NPU development framework, execution path and debuggingDevelop compatibility with AI frameworks (Triton, PyTorch, JAX)Write high-quality, well-tested code with comprehensive documentationCollaborate with other teams (Hardware, Network, QA, AI Framework Integration)Participate in code reviews and technical design discussions Required Qualifications5+ years of experience in distributed system programming3+ years of experience with NPU programming (Triton, CUDA, HIP, OpenCL)Expert-level C/C++ programming with focus on performance optimizationExpert-level Python programming with focus on DL/ML frameworks (PyTorch/JAX/etc)Deep understanding of NPU architecture, memory tiering, and programming modelsKnowledge of N
