Top Remote Infrastructure Engineer Jobs

Build and maintain CI/CD pipelines, cloud infrastructure, Terraform automation, database systems, monitoring, and observability on Google Cloud. Support reliability, security, compliance, incident response, and shared on-call operations while applying backend engineering expertise to production services. The role manages PostgreSQL, Redis, BigQuery, Bigtable, and Firestore environments and contributes to disaster recovery and operational tooling.

The Infrastructure Engineer will build system-level software, focusing on distributed systems and OS level primitives while ensuring scalability and performance of services.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

The role involves leading engineering efforts for ML infrastructure, optimizing distributed training, and enhancing AI platform efficiency in drug discovery.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Design, build, and operate scalable AWS-based platform infrastructure for data processing. Improve pipeline automation, monitoring, security, and reliability. Lead CI/CD, IaC (Terraform/Scalr), network design, and incident response; collaborate with cross-functional teams and participate in on-call rotation. Integrate AI tools to streamline development and troubleshooting.

Lead design and delivery of self-service platform capabilities: provisioning, deployment, observability, and multi-region networking. Drive RFCs and cross-team alignment, build APIs (primarily in Go), implement Terraform/GitOps flows, improve EKS reliability and Grafana-based SLOs, and reduce operational toil via safe, auditable automation. Own on-call rotation and platform adoption outcomes.

The Infrastructure Engineer will ensure the reliability, security, and performance of Thatch's healthcare platform infrastructure. Responsibilities include designing infrastructure, improving CI/CD tools, and managing security and reliability across the stack.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Design, build, test, and continuously deploy scalable infrastructure and platform tooling. Create technical specs and rollout plans, collaborate with stakeholders, mentor teammates, support services in production, and shape engineering practices across teams.

The Senior Infrastructure Engineer will design, implement, and manage scalable cloud and on-premise infrastructure, emphasizing security and automation, collaborating with cross-functional teams.

Build and operate Mercury’s infrastructure platform across AWS, networking, container orchestration, observability, security, and infrastructure as code. Develop AI-friendly platform capabilities for engineers and agents, deliver infrastructure projects, write RFCs and technical specifications, contribute to open-source cloud-native tools, and support secure, scalable, maintainable systems. The role requires troubleshooting Linux and production issues, writing code, and collaborating closely with product engineering teams.

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.

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, 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.

Designs, deploys, operates, and supports virtualization, cloud, network, Linux, and Windows infrastructure across internal and customer environments. Manages Proxmox clusters, Ceph storage, Azure infrastructure, migrations, backups, monitoring, capacity planning, and incident remediation. Builds automation and standardized configurations while enforcing change control, rollback planning, security, reliability, and documentation. The role also supports infrastructure procurement, disaster recovery testing, and scalable, repeatable platform delivery.

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.

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.

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.

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