Top Remote Infrastructure Engineer Jobs

Develop and maintain components for machine learning inference platform. Focus on Kubernetes deployments, resource management, and performance monitoring.

Loti AI seeks a DevOps engineer for decentralized infrastructure to deploy, scale, and maintain distributed systems, manage IPFS clusters, and oversee blockchain nodes.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

The Engineering Director will lead and manage the cloud infrastructure and developer enablement, focusing on AI-native systems, team development, and security operations.

As a System Engineer, you'll design, deploy, and maintain cloud systems for AI workloads, troubleshoot complex issues, and collaborate with teams to optimize performance.

Design, deploy, and maintain highly available, secure cloud platforms; support CI, automation, monitoring, and migrations of legacy systems to AWS; resolve performance, vulnerability, and scalability issues; create operational documentation and collaborate with stakeholders.

Primary point-of-contact for a customer, providing on-site and remote HPC, Ethernet, and AI infrastructure support. Troubleshoot Linux systems, networking protocols, and interoperability; reproduce and resolve complex issues; collaborate with engineering, marketing, and support; document support methodologies and improve processes.

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, build, and own scalable platform infrastructure for low-latency voice/ML services. Anticipate and eliminate scaling bottlenecks, improve developer experience and deployment speed, integrate product with ML systems (credentials, proxy, routing, experimentation), and set technical direction as the platform team grows.

The role involves managing full technical lifecycle of client cloud deployments, designing integrations, maintaining infrastructures, and engaging with clients directly.

Design and lead cloud and AI infrastructure solutions for commercial customers: modernize platform estates, migrate workloads (Windows, Linux, Oracle), design hybrid networking and secure architectures, run workshops, PoCs and pilots, and influence Azure-based deployment decisions to enable Frontier AI transformation and compliance.

Serves as the primary technical partner for customers deploying and operating GPU clusters and AI infrastructure. Responsibilities include configuring and tuning GPU environments, troubleshooting hardware, networking, operating system, and cluster issues, coordinating with internal engineering teams, translating requirements into architectures and execution plans, documenting solutions, and identifying performance and reliability improvements.

Design and build scalable backend components and indexing pipelines for semantic and hybrid retrieval, build retrieval orchestration and knowledge-graph services, improve retrieval quality via evaluation and observability, design APIs, and optimize latency, throughput, cost, reliability, and security for large-scale AI inference and retrieval workloads.

Build and maintain cloud infrastructure, automation, configuration management, and CI/CD pipelines. Operate Kubernetes, Helm, and Docker workloads across multiple public clouds; configure observability tooling; troubleshoot production systems spanning networking, databases, serverless functions, and caching; and participate in a 24/7 on-call rotation. The role also involves owning infrastructure projects end to end and collaborating across engineering and product teams.

Lead platform reliability: define SLOs/error budgets, own observability and deploy pipelines, harden integrations with dental systems, operate LLM-driven workflows safely, build incident practices, and raise engineering reliability across the company.

Design, build, and maintain scalable, resilient data platform services for data integration, event processing, and orchestration. Improve data availability, discoverability, dependability, and security tooling while collaborating across cross-functional teams and delivering production-grade solutions at scale.

Design, develop, and maintain high-performance distributed storage software and protocol APIs (file, block, object). Build scalable, resilient storage services, integrate with hardware (NVMe, GPU-direct), troubleshoot production data center issues, and participate in full SDLC for on-prem storage solutions.

Lead design and implementation of high-performance distributed storage systems across object, block, and file paradigms. Drive architecture, mentor engineers, integrate storage with networking/compute/DPUs, optimize protocol performance, troubleshoot production data center issues, build benchmarking and observability tooling, and collaborate on cross-functional AI infrastructure deployments.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

The Senior Solutions Engineer will design and implement infrastructure for AI and HPC workloads, engage with customers, and lead technical discovery and architecture design.

Build and operate Nebius's network infrastructure: define SLIs/SLOs, improve site and inter-site reliability, lead incident response and postmortems, develop observability and alerting, automate change workflows, and collaborate with network and platform teams to embed operability.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Senior individual contributor who defines multi-cloud infrastructure standards and reference architectures, authors reusable Terraform IaC, drives Kubernetes production patterns, leads CI/CD and observability practices, advises on security and cost optimization, mentors senior engineers, and shapes pre-sales technical solutioning across AWS, GCP, and Azure.

Design, build, and operate Rust services powering Scribd’s large-scale Content Library and core infrastructure. Own features end to end, evolve Python tooling, optimize DataFusion, Parquet, S3, caching, and PostgreSQL/Aurora systems, and strengthen governance through lineage, authorization, and deletion controls. Lead cross-functional technical projects, support platform users, mentor engineers, improve developer workflows, and maintain production reliability through observability, on-call, and incident response.

Deploy and implement enterprise infrastructure solutions across compute, storage, networking, and virtualization environments. Perform rack-and-stack installations, cabling, system integration, hardware deployment, and Layer 1/Layer 2 troubleshooting. Work directly with customers at data centers and colocation facilities, document implementations, provide knowledge transfer, and independently manage deployment priorities. The role requires approximately 60% regional travel and experience with enterprise networking, storage, SAN, virtualization, and command-line infrastructure platforms.