NVIDIA is hiring a Senior Solutions Architect to work hands-on with cloud partners to solve Day 2 operations challenges for large-scale GPU and AI infrastructure. The role requires substantial production infrastructure experience (12+ years, or 5+ years of exceptional specialist-level work) and hands-on knowledge across GPU/HPC/cloud stacks (e.g., Kubernetes/Slurm, Prometheus/Grafana/OpenTelemetry, Terraform/Ansible/Argo CD, Linux, Python/Bash) and storage/networking/GPU technologies. The posting lists an expected base salary range of 224,000–356,500 USD per year.

Work on design, deployment, automation, monitoring and troubleshooting of large-scale HPC/AI clusters (bare metal to software stacks). Requires a degree (or equivalent) and 8+ years of experience with HPC/AI technologies, job schedulers (e.g., Slurm), orchestration (K8s), Linux/Windows, storage (Lustre, GPFS, Weka.io), networking (InfiniBand, Ethernet) and scripting/automation (Python, Bash, Jenkins, Ansible, etc.).

NVIDIA seeks a Senior MLOps Engineer to collaborate with strategic customers to implement and optimize AI workloads, profile and tune large-scale training and inference, build MLOps pipelines, and support new hardware and open-source frameworks. Requires a BS/MS/PhD or equivalent, 8+ years technical experience, strong systems knowledge (Linux, Kubernetes, batch schedulers, distributed filesystems, datacenter networking), scripting and systems programming (Python, bash, C++/Go/Rust), and experience with deep learning frameworks. Listed salary ranges: $184,000–$287,500 (Level 4) and $224,000–$356,500 (Level 5).

Work on design, development, and operation of multi-tenant cloud SDN control and data plane software (OVS/OVN/OpenFlow) for NVIDIA AI Cloud. Role requires strong programming in C and Go, scripting in Bash/Python, Kubernetes/CNI experience, CI/CD and GitOps experience, and 8+ years in large-scale distributed environments. Location: US (Santa Clara) / Remote. Listed base salary range: 184,000–287,500 USD per year.

Hands-on role partnering with engineers running AI clouds to diagnose and solve production Day 2 problems, prototype and validate fixes, and convert solutions into reusable operating procedures, reference architectures, automation, and telemetry. Requires extensive production infrastructure/cloud/SRE experience (12+ years or 5+ years for exceptional specialists) and hands-on experience with GPU/HPC/cloud technologies (e.g., Kubernetes, Prometheus, Terraform, DCGM, high-performance storage and networking).

Role to develop and improve integration, build, simulation, and pre-submit qualification flows for large ASIC IP; improve CI reliability, debuggability, and scalability; apply AI workflows where useful; and collaborate with designers, verification, firmware, methodology, and infrastructure teams. Requires BS/MS (or equivalent), 5+ years industry experience, familiarity with ASIC workflows, UVM, Verilog/SystemVerilog, simulation/debug tools (VCS/Verdi/Xcelium), and scripting (Python/Perl).

NVIDIA seeks a Principal Software Engineer to lead technical strategy and execution for production-grade Kubernetes lifecycle systems and core platform capabilities (cluster management, control plane services, fleet lifecycle, day-2 operations) across cloud and on-premises. Requires 15+ years of relevant software engineering experience, deep Kubernetes and distributed systems expertise, strong systems programming skills (Go/Python/Rust/C++), and leadership/mentoring experience. Posted salary range: 272,000–431,250 USD per year.

Responsible for crafting, scaling, and hardening deep learning infrastructure and libraries for multi-thousand GPU training clusters, improving data loaders, distributed training, scheduling, and performance monitoring, and owning orchestration and fault-resilient training systems. Requires 12+ years building high-performance distributed systems, strong Python and PyTorch experience, large-scale training expertise (DDP/FSDP, NCCL, parallelism), and systems knowledge including datacenter networking, Lustre, Slurm/Kubernetes, and storage.

NVIDIA is hiring a Senior AI/ML HPC Cluster Engineer to lead day-to-day operations, reliability, and performance optimization of on-premises and cloud GPU clusters. The role requires a minimum of 5 years' experience with large-scale compute infrastructure and hands-on skills with schedulers (e.g., Slurm, K8s, PBS), Linux administration, configuration management, container technologies, Python/bash scripting, and MPI-based workflows.

Build, operate, and automate cloud-native platform services (Python/Go) on Kubernetes to support DGX Cloud and SuperPod deployments. Responsibilities include infrastructure orchestration, self-service workflows, integrations, observability, security, and collaboration with networking and infrastructure teams. Requires BS or equivalent and 8+ years of relevant experience; experience with Terraform, Ansible, Temporal, databases, messaging (Kafka/NATS/SQS), Prometheus/Grafana/OpenTelemetry, and networking/security technologies.

Join NVIDIA's Performance and R&D teams to profile and benchmark AI and HPC workloads on GPU and CPU clusters, analyze networking and system-level performance (RDMA, MPI, NCCL, RoCE), develop diagnostic and benchmarking tools, and collaborate with hardware, firmware, networking, and software teams to produce reproducible performance insights.

Experienced software engineer to develop and maintain automation tooling and orchestration for NVIDIA's GPU Cloud and SuperPod deployments. Responsibilities include building network design and configuration management solutions, integrating with cloud provider APIs, working with DCIM tools (NetBox/Nautobot), implementing containerized services (Kubernetes/EKS), and developing streaming/event pipeline architectures. Requires strong background in networking, network automation, Python and Go, and 12+ years of relevant experience.

The Senior Manager, Storage Production Engineering will manage and grow a team responsible for designing, deploying, and operating large-scale storage systems (distributed, parallel file systems, object/NAS). The role requires strong hands-on storage experience, automation and monitoring skills, incident management, capacity planning, and at least 12 years of relevant experience with 6+ years of people/technical leadership.

Role focuses on analyzing end-to-end performance of large-scale AI workloads, designing and executing performance studies and benchmarks, using profiling and observability to diagnose bottlenecks, and driving optimizations with platform and GPU architecture teams. Requires BS (or equivalent), 12+ years' experience, strong C++ and Python skills, and foundations in operating systems, computer architecture, distributed systems, and performance engineering. Experience with CUDA, GPU systems, and deep learning frameworks (PyTorch, JAX/XLA) is highlighted.

Join the DGX Cloud team to operate large object storage deployments end-to-end — from API and metadata tiers to data placement, replication, and erasure coding. Responsibilities include defining SLOs, automating fleet lifecycle (bare-metal provisioning, drive onboarding, rebalancing, firmware/kernel updates), shipping storage software via Ansible/Kubernetes and CI/CD, capacity forecasting, performance tuning for AI workloads, and participating in global on-call rotations with clear postmortems and runbooks. Requires deep object storage experience, strong Linux and automation skills, and 8+ years in SRE/production engineering with 3+ years running distributed object storage at petabyte scale.

Role owns lifecycle and automation of Kubernetes environments for network systems across data centers and cloud, including cluster provisioning, upgrades, GitOps delivery, observability, capacity, and production on-call/incident response. Requires 8+ years of relevant experience, deep Kubernetes expertise, proficiency in a general-purpose language (Go or Python), and experience with GitOps, IaC, CI/CD, and network automation/telemetry on Kubernetes.

Validate and operationalize NVIDIA AI factory environments from first rack power-on to customer handoff. Work on multi-GPU/multi-node Linux clusters running AI/LLM workloads using NCCL and collectives (AllReduce, AllToAll); own benchmark execution, observability, automation (Python, Shell), and cross-functional troubleshooting. Requires a BS or equivalent and 6+ years managing Linux-based systems in HPC/distributed/AI settings. Location: US (remote / multiple US locations). Salary range provided by employer: 148,000–235,750 USD/year.

Provide technical support, debugging, and issue resolution for NVIDIA Ethernet switching technologies and end-to-end AI solutions (e.g., Spectrum-X). Act as primary contact for a customer, collaborate with engineering and marketing, develop support methodologies. Requires 6+ years of customer support/debugging experience, strong Linux and enterprise networking skills, and regular on-site presence in College Station, TX.

Provide primary customer-facing technical support and troubleshooting for Ethernet and AI infrastructure (including NVIDIA Spectrum‑X). Role requires 5+ years of hardware and software support experience, strong Linux system administration and networking skills (L2/L3, BGP/OSPF, VXLAN/EVPN, TCP/UDP), packet analysis (tcpdump/Wireshark), and collaboration with engineering and product teams. Salary ranges provided in the posting: USD 108,000–172,500 (Level 3) and USD 120,000–207,000 (Level 4).

Remote Senior Solutions Architect role on NVIDIA's Infrastructure Specialists team responsible for end-to-end observability of HPC and AI factory systems. Responsibilities include running validation tools and microbenchmarks, defining health metrics and thresholds, extending telemetry across hardware and fabric, building automation (Python, Shell), working with observability stacks (Prometheus, Grafana, Loki), and collaborating with cross-functional teams to prepare systems for deployment. Requires 6+ years managing Linux-based systems in HPC or large AI/ML environments and hands-on multi-GPU/multi-node cluster experience.