Future Tech Jobs
Future Tech Jobs
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.Today, we’re tapping into the unlimited potential of AI to define the...
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.Today, we’re tapping into the unlimited potential of AI to define the...
This role involves driving performance and scalability of the NVIDIA DGX Cloud software stack, focusing on Kubernetes and NVIDIA AI infrastructure. You'll diagnose complex distributed systems issues, develop automated testing and monitoring tools, and contribute to open-source communities. The position emphasizes end-to-end optimization from orchestration to hardware, supporting large-scale AI workloads in cloud environments.
This role involves driving performance and scale characterization of the NVIDIA DGX Cloud software stack, focusing on Kubernetes and NVIDIA components like GPU Operator and DCGM. The engineer will deep-dive into distributed systems issues, build automated testing and monitoring tools, and contribute to open-source communities to optimize large-scale AI infrastructure.
This role involves deep performance and scalability analysis of the NVIDIA DGX Cloud software stack, focusing on Kubernetes and NVIDIA components like GPU Operator, DCGM, and NIM. The engineer will diagnose complex distributed system issues, build automated testing and monitoring tools, and contribute to open-source communities. Work includes optimizing large-scale AI infrastructure for cost and efficiency across cloud platforms.
This role involves driving performance and scalability for NVIDIA's DGX Cloud Kubernetes-based AI infrastructure. The engineer will diagnose complex distributed systems issues, optimize large-scale GPU clusters, and build automated testing and monitoring tools. Collaboration with AI teams and open-source communities is key to advancing AI workload efficiency.
This role involves driving performance and scale characterization for NVIDIA's DGX Cloud software stack, focusing on Kubernetes and NVIDIA components like GPU Operator, DCGM, and distributed inference. The engineer will diagnose complex distributed system issues, build automated testing and monitoring tools, and collaborate with AI teams and open-source communities to optimize large-scale AI infrastructure. A strong background in systems software, Kubernetes, and performance at scale is essential.
This role involves driving performance and scalability for NVIDIA's DGX Cloud software stack, focusing on Kubernetes and NVIDIA components like GPU Operator and DCGM. The engineer will diagnose complex distributed systems issues, build automated testing and monitoring tools, and contribute to open-source communities. The position emphasizes optimizing large-scale AI infrastructure across cloud platforms.
This role involves driving performance and scale characterization for NVIDIA's DGX Cloud software stack, focusing on Kubernetes and NVIDIA components like GPU Operator and DCGM. The engineer will diagnose complex distributed systems issues, build automated testing and monitoring tools, and collaborate with AI teams and open-source communities to optimize large-scale AI infrastructure. Key responsibilities include root cause analysis, CI/CD integration for continuous testing, and contributing to upstream projects like Kubernetes and CNCF.
This role involves designing and building scalable reinforcement learning (RL) post-training infrastructure that supports the full lifecycle of training, inference, and rollout across heterogeneous hardware. You'll optimize distributed systems for performance, fault tolerance, and efficiency, working closely with AI researchers and contributing to open-source frameworks like VeRL, Miles, and TorchTitan. The role emphasizes deep collaboration across teams to advance AI capabilities through systems innovation.
This role involves designing and building scalable reinforcement learning post-training infrastructure that operates efficiently from single-GPU experiments to large-scale distributed systems. You'll optimize training-inference-rollout loops across heterogeneous hardware, contribute to open-source RL frameworks like VeRL and TorchTitan, and collaborate with AI researchers and infrastructure teams to improve distributed runtimes such as Ray and Monarch. The position emphasizes fault tolerance, elastic scaling, and integration with next-generation hardware and deep learning tools.
This role involves designing and building scalable reinforcement learning (RL) post-training infrastructure that supports the full lifecycle of training, inference, and rollout across heterogeneous hardware. The engineer will contribute to open-source RL frameworks, optimize distributed systems performance, and collaborate with research and hardware teams to advance AI capabilities. Key challenges include fault tolerance, elastic scaling, and efficient coordination of models across large-scale GPU, CPU, and LPU environments.
This role involves designing, implementing, and maintaining large-scale HPC and AI clusters, working with the latest GPU and deep learning technologies. You will collaborate with researchers, developers, and customers to optimize system performance and develop new solutions.
This role involves designing, implementing, and maintaining large-scale HPC and AI clusters, including monitoring, logging, and alerting. You will work with the latest GPU and deep learning technologies, develop CI/CD pipelines, and collaborate with researchers and developers to improve workflows and create innovative solutions.
This role involves designing, implementing, and maintaining large-scale HPC/AI clusters, managing job/workload schedules, and developing CI/CD pipelines. You will work closely with HPC, OS, GPU compute, and systems specialists to architect and optimize performance platforms, and support R&D activities and POCs.
