Top Remote Infrastructure Engineer Jobs

As a Software Engineer on the AI team, you will manage data collection, extend cloud infrastructure, and collaborate on dataset roadmaps for model training operations.

This Software Engineer role focuses on data collection for model training at Speechify, requiring skills in data ingestion, cloud infrastructure, and collaboration with AI team members.

The Software Engineer will enhance data ingestion pipelines, collaborate with AI scientists, and integrate new audio data sources, all within a cloud infrastructure.

The Software Engineer will manage data collection for model training, optimize cloud infrastructure on GCP, and collaborate with AI team members to enhance dataset quality and volume.

The Software Engineer will handle data collection for AI model training, optimize the data ingestion pipeline, and collaborate with scientists to improve data quality and scale.

The Software Engineer will enhance data collection processes, manage cloud infrastructure for ingestion pipelines, and collaborate on dataset roadmaps for AI products.

The Software Engineer will manage data collection for model training, operate cloud infrastructure, and collaborate on dataset roadmaps to enhance data quality for Speechify's products.

The Software Engineer will support data collection for model training by enhancing cloud infrastructure and collaborating on data workflows, focusing on efficiency and scalability.

The role involves supporting data collection and ingestion pipeline operations for AI model training, including collaboration on dataset roadmap and cloud infrastructure management.

The Software Engineer will manage data collection for AI model training, extend cloud infrastructure on GCP, and collaborate on data strategy.

As a Software Engineer for Data Infrastructure & Acquisition at Speechify, you will focus on data collection, operate cloud infrastructure, and collaborate with the AI team to enhance data processing for model training.

The Software Engineer will manage data collection for model training, operate cloud infrastructure, and collaborate on AI datasets.

The Software Engineer for Data Infrastructure & Acquisition will handle data collection for model training, manage cloud infrastructure on GCP, and collaborate with the AI team to enhance data quality and scalability.

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.

Design, build, and operate cloud infrastructure and SRE capabilities for an enterprise AI platform. Responsibilities include infrastructure-as-code, landing zones, networking, Kubernetes, CI/CD, observability, incident response, SLOs, production readiness, automation, cost optimization, and support for hybrid, edge, air-gapped, and customer-controlled environments. The role is remote, client-facing, and requires strong collaboration and reliability ownership.

Design, deploy, operate, and automate reliable large-scale EDA infrastructure services on NVIDIA hardware. Build tools that reduce manual work, support distributed private and public cloud systems, define service-level objectives, improve observability, and participate in incident response and on-call rotations. The role also provides systems-design guidance to peer teams and may involve Kubernetes, OpenStack, Docker, Slurm, multi-cloud infrastructure, networking, storage, and GPU-based systems.

Own and scale Gumloop’s infrastructure for agent orchestration, sandboxing, and hosted MCP services. Architect large-scale systems, maintain Kubernetes clusters, CI/CD pipelines, and GCP cloud infrastructure, and improve reliability, security, observability, availability, and performance. The role also contributes to technical roadmaps, customer discussions, and close collaboration with founders in a high-ownership startup environment.

Leads technical customer engagements for cloud, AI, infrastructure, and security transformations. Advises customers, conducts solution demonstrations and proof-of-concept sessions, supports complex integrations and deployments, shares technical expertise through mentoring and events, and develops strategic customer success and win plans with account and marketing teams.

Design, deploy, and operate scalable multi-cloud data and AI platform infrastructure. Build IaC, CI/CD pipelines, containerized workloads, self-service ML provisioning, observability, and secure production practices while driving architecture, standards, and mentoring.

Build and operate multicloud Kubernetes infrastructure and platform capabilities, including cloud foundations, networking, identity, cluster lifecycle, disconnected deployments, packaging, GitOps delivery, infrastructure as code, observability, upgrades, and incident response. Partner with engineering teams to provide reusable self-service infrastructure primitives across commercial, government, secure, classified, and disconnected environments.

Build and operate multicloud Kubernetes infrastructure and platform capabilities across commercial, government, classified, and disconnected environments. Responsibilities include cloud foundations, networking, identity, cluster lifecycle, packaging, GitOps delivery, infrastructure-as-code, policy enforcement, staged rollouts, observability, incident response, and backup/recovery. The engineer will also create self-service infrastructure primitives for internal engineering teams and support reliable fleet-scale deployments.

Design, develop, and maintain enterprise infrastructure automation and Infrastructure as Code solutions across cloud, operating systems, virtualization, storage, databases, networking, and observability platforms. Build reusable frameworks, APIs, self-service capabilities, provisioning and patching workflows, compliance validation, security remediation, and operational dashboards. Collaborate with platform, cloud, security, architecture, and operations teams while documenting automation standards and engineering practices.

The Senior Software Engineer will lead and support a team in developing reliable and scalable backend systems, collaborating on product development, and improving infrastructure for secure communication between services.

Design and operate large-scale infrastructure supporting GPU- and CPU-based EDA workloads. Build automation for provisioning, configuration, deployment, lifecycle management, monitoring, health remediation, cluster enrollment, and recovery across Linux compute fleets. Integrate services with schedulers, infrastructure systems, and observability platforms. Participate in incident response, root-cause analysis, capacity planning, and reliability improvements while collaborating with EDA, networking, storage, and hardware teams.

Support and administer RPA platform infrastructure and enterprise servers (Windows/Linux); manage Active Directory, scripting (PowerShell/Bash), patching, firewalls, DNS/TCP-IP and SSL/TLS; support VMware and cloud (Azure/AWS); develop, test, and troubleshoot automation solutions; participate in on-call rotation and collaborate with teams to modernize workflows.