Top Remote Cloud Engineer Jobs

Lead hands-on storage software engineering for large-scale GPU clusters. Contribute to open-source parallel and distributed file systems, write and review production code, troubleshoot I/O, metadata, corruption, and recovery issues, validate performance and durability through scale testing, and establish configuration and operational standards. Collaborate with AI, SRE, networking, security, cloud, and storage teams while supporting high-performance storage across cloud, neocloud, and on-premises environments.

Develop and scale production AI infrastructure supporting large GPU clusters and workloads. Responsibilities include Kubernetes operator and API development, GPU resource scheduling, cluster operations, node health monitoring, telemetry-based reliability improvements, incident analysis, and distributed systems engineering. The role collaborates across teams to improve the performance, availability, and scalability of NVIDIA’s DGX Cloud platform.

Build and operate reliable, scalable production software, automation, and infrastructure for NVIDIA DGX Cloud AI inference and agentic workloads. Responsibilities include Kubernetes and multi-cloud deployments, infrastructure as code, GitOps, observability, SLI/SLO definition, capacity management, incident response, safe rollouts, recovery automation, and reducing operational toil. The role collaborates across platform, networking, storage, security, model, and GPU infrastructure teams.

Build and operate software automation for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware and software upgrades, cluster lifecycle management, BMC and Redfish tooling, failure diagnosis, observability, incident response, and automated repair. The role supports NVL72 systems, BlueField DPUs, Linux, Kubernetes, and cloud or on-premises environments while coordinating across hardware, networking, platform, data center, and partner teams.

Build and operate software automation for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware upgrades, repair workflows, cluster lifecycle management, BMC and Redfish tooling, and NVIDIA NVL72 and BlueField DPU operations. Diagnose failures across hardware, Linux, networking, and Kubernetes; support incident response, on-call operations, observability, root-cause analysis, and permanent remediation across cloud and on-premises environments.

Build and operate automation and tooling for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware and software upgrades, cluster lifecycle management, BMC and Redfish integration, failure diagnosis, reliability engineering, incident response, and automated repair. The role works across hardware, networking, Linux, Kubernetes, data center operations, and partner teams to safely bring GPU capacity into production.

Build and scale NVIDIA’s AI infrastructure platform for GPU provisioning, configuration, lifecycle management, monitoring, health management, and distributed-system qualification. Develop automation for data center operations and machine learning systems, integrate hardware through AI applications, and work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, Linux, and fault-tolerant distributed architectures.

Design and implement cloud-native data management services (catalog, metadata, dataset/checkpoint lifecycle) for exabyte-scale GPU workloads. Build backend systems on Kubernetes and major cloud providers, collaborate with research and cross-functional teams, document architecture, and drive integration with storage/compute innovations (GPU Direct Storage, DPU).

NVIDIA seeks a Senior Network Engineer to develop cloud network infrastructure. Responsibilities include architecting, implementing, and automating scalable network solutions for software development workflows.

Develop and debug cloud-native solutions for AI/ML datacenters, focusing on Kubernetes and Slurm enhancements and customer workflows.

Develop and maintain web-based storage management applications, including Kubernetes operators and CSI plugins, collaborating closely across teams to define APIs.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Senior software engineer on Developer Tools working across frontend and backend to design, implement, and ship profiling features for cloud and cluster environments. Collaborate with cross-functional teams, interact with users, and deliver end-to-end product features from prototypes to demos focused on collecting, analyzing, and visualizing performance data.

The role involves architecting and operating NVIDIA's cloud-native attestation platform, developing high-availability services, collaborating with cross-functional teams, and mentoring engineers.

The Senior Network Reliability Engineer will support cloud and datacenter networks, manage incidents, engage with vendors, and drive operational improvements.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Design, implement, and operate large-scale, high-performance distributed storage clusters for AI/HPC workloads. Build monitoring, logging, alerting, automation, and capacity management; optimize latency, throughput, caching, tiering, and data placement. Support lifecycle from design to production, perform incident response and blameless RCA, and participate in on-call rotation to maintain availability, performance, and security.

Lead the design, deployment, and operation of network security infrastructure across hybrid on-premise and multi-cloud environments. Manage firewalls, load balancers, IDS/IPS, IAM controls, monitoring, vulnerability remediation, compliance, and incident response. Partner with architecture, security, and operations teams to improve observability, threat detection, resilience, and operational readiness across hyperscale networks.

Partner with customers across the DGX Cloud lifecycle to drive adoption and performance. Translate workload and business requirements into scalable compute, networking, storage, and cloud recommendations. Create reusable playbooks, tooling, and dashboards. Lead architecture reviews, performance tuning, cloud portability, and GPU resource planning. Use usage data to identify risks and opportunities and deliver lightweight automation to accelerate delivery and operations.

Lead performance characterization and optimization of large-scale AI workloads across GPUs, networking, storage, and software. Design and run benchmarks and profiling, diagnose bottlenecks, define evaluation methodologies and metrics, produce actionable optimization plans, and collaborate with DL engineers, platform teams, and GPU architects to deliver improvements.

Build and maintain network automation and orchestration software for NVIDIA GPU Cloud and SuperPod deployments. Design/configure centralized configuration management, integrate cloud provider APIs, implement data pipelines and streaming telemetry, and automate lifecycle operations from procurement to deployment. Collaborate with infrastructure experts to deliver zero-touch, production-grade network provisioning, monitoring, and management tools.

Design, build, and operate core cloud-native infrastructure services for NVIDIA DGX Cloud. Develop scalable, secure microservices and automation for provisioning and lifecycle management, integrate internal and external platforms, and implement observability and security to ensure reliable, global production operations.

Leads Azure cloud infrastructure architecture, automation, Kubernetes administration, CI/CD, application hosting, observability, incident response, and responsible AI-enabled engineering. Designs secure, scalable, and cost-efficient infrastructure; develops governed AI service patterns; improves reliability and operational response; mentors engineers; and contributes to agile technical leadership.

The Senior Cloud Security Engineer identifies and mitigates cloud vulnerabilities, monitors environments for breaches, responds to incidents, assesses security weaknesses, and strengthens cloud security controls. The role develops risk management programs, conducts gap analyses, optimizes cloud security tools, improves monitoring and response capabilities, tracks emerging threats, and advises stakeholders on security policies and standards. The position supports the National Science Foundation and requires an active Public Trust clearance.

Modernize legacy Windows-based .NET applications into secure, scalable containerized services on AWS EKS. Responsibilities include Kubernetes deployment support, PostgreSQL performance tuning, observability with Dynatrace, Splunk, and CloudWatch, application security remediation, Terraform and Terragrunt infrastructure automation, CI/CD, production deployments, and cross-layer troubleshooting.