Future Tech Jobs
Future Tech Jobs
We are seeking a highly technical and strategic Developer Relations Manager to join our team, with a focus on engaging developer ecosystems across the telecom domains. In this pivotal role, you will work directly with software solution providers, developers, academics...
We are seeking a highly technical and strategic Developer Relations Manager to join our team, with a focus on engaging developer ecosystems across the telecom domains. In this pivotal role, you will work directly with software solution providers, developers, academics...
This role involves leading Faculty AI's technical strategy and research in AI safety, with a focus on red teaming, risk mitigation, and developing cutting-edge evaluations for frontier models. The Technical Director will build and lead a high-performing R&D team, shape global AI safety practices in collaboration with leading labs and governments, and translate research into real-world impact. It is a senior leadership position combining deep technical expertise, strategic vision, and influence in the international AI safety landscape.
The Head of AI Assurance will lead the development of a comprehensive AI safety and assurance proposition for the financial and professional services sectors. This involves formulating risk management frameworks, building software tools, and conducting audits to ensure AI systems meet regulatory standards. The role also focuses on generating new business, delivering high-quality client outcomes, and elevating Faculty’s market profile through thought leadership.
This role involves driving performance and scalability of 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 open-source communities to optimize large-scale AI infrastructure.
This role involves optimizing and scaling NVIDIA's DGX Cloud software stack, focusing on Kubernetes and distributed AI infrastructure. The engineer will diagnose performance bottlenecks, build automated testing frameworks, and collaborate with AI teams and open-source communities to enhance large-scale AI workloads. Key responsibilities include end-to-end system analysis, CI/CD integration, and contributing to upstream projects like Kubernetes and CNCF.
This role involves driving performance and scalability of 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. Work includes root cause analysis, CI/CD integration, and contributing to upstream projects like Kubernetes and CNCF.
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.
As a Senior HPC DevOps Engineer, you will design, implement, and maintain large-scale HPC and AI clusters, focusing on state-of-the-art monitoring, logging, and alerting systems. You will work closely with HPC, OS, GPU compute, and systems specialists to architect and develop performance platforms, streamline CI/CD pipelines, and automate complex networking and deployment processes.
