Role owns full lifecycle of GPU compute clusters (procurement, provisioning, config management, monitoring, deprecation) across heterogeneous Linux environments. Requires 5+ years administering large-scale HPC/ML clusters, strong Linux and scripting skills, experience with Slurm, Ansible, storage solutions (NFS/Lustre/WekaFS), container runtimes, and high-speed networking technologies.

Role involves platform bringup, feature enablement, end-to-end software validation and debug for NVLink/NVSwitch rack-scale systems. Responsibilities include building diagnostics, automation, telemetry and SRE-style validation infrastructure, performing stress testing and reliability/MTBI validation, triaging cross-domain software/firmware/hardware/network issues, and collaborating with architecture, firmware, hardware and customer teams. Requires 5+ years experience and strong skills in C/C++, Python, system-level debugging, and networking (TCP/IP, Ethernet, RDMA/RoCE).

Lead technical strategy and implementation for DGX Cloud cluster operations, including automation, GitOps, lifecycle management, validation, repair, upgrades, observability, SLOs, and incident response. Requires 15+ years building and operating large-scale distributed systems or cloud infrastructure, deep Kubernetes and Linux experience, and strong programming skills in Go or Python. Role involves mentoring engineers and influencing cross-functional platform, infrastructure, storage, networking, and security teams.

Role at NVIDIA to debug and enhance cloud-native stacks for multi-rack, multi-tenant AI/ML datacenters (Kubernetes + Slurm), prototype feature extensions (operators, Slurm plugins, microservices), build reproducible testbeds (Helm/Ansible/Terraform), and collaborate with CSPs and internal teams on RFCs and upstream contributions. Requires 10+ years experience in distributed systems and expertise with GPUs, networking (RDMA/RoCE), CI/CD, and observability.

NVIDIA seeks a Senior Network Reliability Engineer for DGX Cloud to operate and maintain large-scale on‑premises and cloud networks. Responsibilities include 24/7 shift-based incident response, alert remediation within SLAs, vendor engagement, network device upgrades and capacity augmentations, and contributing to automation and monitoring tooling. Requires deep knowledge of TCP/IP and routing/switching protocols, experience with CSPs (AWS/Azure/GCP/OCI), familiarity with vendors like Arista/Juniper/Fortinet, and 5+ years in network operations.

Role focuses on building performance testing frameworks, automated CI/CD pipelines, benchmarking systems, and dashboards to track and analyze GPU and compiler performance across models and hardware. Requires 5+ years software engineering experience, strong Python (C++ a plus), familiarity with deep learning frameworks (PyTorch/TensorFlow/JAX/TensorRT), GPU/hardware-aware performance analysis, profiling, and debugging across software and hardware layers. Base salary range listed: 152,000 USD–241,500 USD.

Work on architecting and building reinforcement-learning post-training infrastructure that scales from single-GPU experimentation to production across thousands of nodes. Responsibilities include optimizing training-inference-rollout loops on GPUs/CPUs/LPUs, improving RL frameworks (e.g., VeRL, Miles, TorchTitan), ensuring fault tolerance and elastic scaling, and collaborating with researchers and hardware/software teams. Requires MS/PhD or equivalent experience and 5+ years in distributed systems, HPC, deep learning infrastructure, or ML systems engineering.

Responsible for developing and maintaining continuous development systems and tooling across the software stack (from user-facing frameworks to compilers and drivers). Requires MS/PhD or equivalent experience, 5+ years working on large software projects, proficiency in C++ and/or Python, scripting, Linux, and experience with CI/CD and cloud-hosted developer environments.

NVIDIA seeks a Senior AI Compute Engineer to join Infrastructure Specialists, working with customers and internal teams to plan, deploy, and validate Linux-based AI Compute/HPC systems. Requires 8+ years of deployment/support experience, strong Linux administration, scripting (Bash/Python/Ansible), cluster provisioning, schedulers (SLURM/LSF/UGE), benchmarking (HPL/NCCL/MLPerf), Kubernetes, and up to 20% US travel.

Role requires designing, deploying, and operating large-scale AI/HPC networking and infrastructure (Neo Clouds) with emphasis on performance, monitoring, automation, and customer engagement. Requires 5+ years in networking, expertise in EVPN/BGP/OSPF/VXLAN, hands-on experience with Cumulus/SONiC/IOS/Junos/EOS, and automation using Ansible/Salt/Python. Must have strong English communication skills.

Role to design, build, deploy and operate reliable large-scale infrastructure platform services atop NVIDIA hardware. Responsibilities include infrastructure automation, distributed systems design, defining SLOs/error budgets, observability, incident response (on-call), and consulting with peer teams. Requires BS or equivalent and 12+ years of relevant experience. Locations listed in the US with remote options. Salary ranges provided per level.

NVIDIA is hiring a Senior AI/HPC Engineer on the infrastructure Specialist team to deploy, manage and maintain AI/HPC GPU infrastructure in Linux environments. The role involves customer-facing architecture and implementation, cluster and network optimization (InfiniBand/Ethernet), scheduler and MPI/NCCL expertise, automation (Ansible/Salt/Puppet), Kubernetes, and documentation/knowledge transfer. Requires a related degree or equivalent and 5+ years of deployment/support experience.

Role responsible for improving ML training and inference efficiency across GPU clusters by debugging and optimizing software and infrastructure, building tools and frameworks to detect bottlenecks, collaborating with researchers across domains (LLMs, robotics, autonomous vehicles, video), and working with technologies such as CUDA, NCCL, Nsight, distributed training, cloud platforms, and fast distributed storage.

Role requires conducting in-depth performance analysis on large multi-GPU/multi-node HPC clusters, studying HW/SW interactions, implementing micro-benchmarks (C/C++), building analysis tools, and triaging customer performance issues. Qualifications: MS or PhD (or equivalent), 3+ years experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM). Familiarity with Python, containers and scheduling tools (Kubernetes, SLURM), networking (Infiniband/RDMA) and CUDA/DL frameworks is listed as desirable. Location: US (remote; Santa Clara listed). Salary ranges provided for Levels 3 and 4.

NVIDIA is hiring a Senior Systems Software Engineer to lead GPU performance engineering at scale. The role involves designing and implementing performance practices for large-scale GPU infrastructure, collaborating with HW/FW/SW teams, debugging firmware/software, and aligning AI workloads with datacenter systems. Required skills include CUDA, Linux, container and cloud technologies, C/C++/Python/Bash, and experience with HPC and performance analysis; 8+ years of applicable experience is specified.

Work involves developing infrastructure and tools to automate complex software processes, implementing benchmarking and profiling frameworks, and applying deep knowledge of OS internals, device drivers, memory, storage, networking, and high-speed interconnects. Requires 5+ years industry experience, proficiency in C++/Python/Go, experience with distributed systems, CI/CD, containerization (Docker, Kubernetes), and performance/debugging of compute workloads.

Role involves architecting, developing, and operationalizing large-scale GPU-accelerated compute platforms and software stacks, working with internal and external engineering teams and researchers. Requires 8+ years' experience in accelerated computing for datacenter/HPC, programming/scripting (C/C++/Python/Bash), parallel filesystems, and strong communication and teamwork. Salary range (base) provided: 184,000–356,500 USD/year depending on level.

The role develops scalable cloud-native services and APIs to support exabyte-scale, high-performance GPU training and inference workflows, working closely with product, platform, SRE, and partner teams. Requires strong foundation in distributed systems, algorithms, data structures, and experience operating backend or cloud-native services (Kubernetes and cloud providers such as AWS/GCP/Azure) using languages like Go, Python, Rust, C/C++, or Java.

Lead performance benchmarking and optimization for NVIDIA data center products, characterizing AI/HPC workloads at scale, building automation and monitoring tools, identifying bottlenecks across compute/memory/network/storage, and driving tuning and architectural recommendations. Requires advanced degree or equivalent, 8+ years in performance engineering or system architecture, and skills in profiling tools, GPU computing (CUDA), HPC networking, and Python/C++/shell scripting.