Top Remote Platform Engineer Jobs

Design and build core systems for post-booking customer experiences, focusing on AI integration, scalable APIs, and middleware for travel partners.

The Applied AI Engineer will design, build, and deploy enterprise-grade AI agents using LLMs, collaborate with customers, and mentor teams to enhance engineering quality.

The Senior Software Engineer will design, build, and maintain scalable backend services, optimizing event-driven architectures and ensuring system reliability and performance across ZoomInfo's platform.

Design, build, and scale GraphQL and gRPC APIs and SQL queries powering the sportsbook marketplace. Own end-to-end feature delivery, performance diagnostics and remediation, release and deployment windows, monitoring in DataDog, and on-call rotations. Mentor peers, draft technical proposals/specs, and improve platform scalability through load testing, tracing, and production hygiene.

As a Senior DevOps / Platform Reliability Engineer, you will manage CI/CD pipelines, automate infrastructure, operate Kubernetes, and enhance observability while ensuring security and compliance for enterprise systems.

Build and operate the platform: manage EKS/Kubernetes, implement GitOps (ArgoCD), maintain CI/CD (GitHub Actions), define SLIs/SLOs, lead incident response/on-call and post-mortems, design observability (Datadog, Sentry, CloudWatch, Grafana), automate infra with IaC (Terraform/CloudFormation/Crossplane), write Python tooling, and shape platform architecture and self-service workflows.

Design, optimize, and scale multi-GPU infrastructure for GenAI/LLM workloads. Perform GPU profiling and benchmarking, manage Slurm and Kubernetes/OpenShift clusters, enable NVIDIA GPU stack, build GenAI pipelines (fine-tuning, RAG, multi-modal), create IaC templates, and support production deployments and client engagements.

The Client Engine Software Engineer will design and implement complex client-side engines using advanced algorithms, focusing on high-performance and fault-tolerance in a remote team environment.

Leads strategy, architecture, deployment, operations, automation, observability, and reliability initiatives for globally distributed database systems. Partners across engineering and business teams to improve performance, scalability, security, continuity, and operational rigor. Oversees incident troubleshooting, root-cause analyses, release processes, database change management, technology evaluation, roadmap planning, and mentoring while operating databases across Kubernetes, cloud, and bare-metal environments.

Lead design, implementation, automation, and operation of enterprise Okta identity services. Drive application onboarding, federation, lifecycle management, resiliency, monitoring, incident response, governance, and platform automation while mentoring engineers and improving security posture.

The Senior Platform Engineer designs and operates Kubernetes infrastructure, automates tasks, enforces platform standards, and collaborates across teams to improve engineering velocity and scalability.

Lead architecture and implementation of new school safety products from concept through production. Design scalable, secure, resilient distributed systems and integration-heavy platforms involving APIs, identity systems, hardware, and external data sources. Drive technical strategy, mentor engineers, establish engineering standards, and collaborate with Product, Design, and engineering leaders to deliver reliable 0-to-1 products for schools.

Own NVIDIA’s config-as-code foundation for its EDA compute farm. Design LSF deployment schemas, build federated deployment pipelines with staged rollout and rollback, eliminate configuration drift, and create regression testing for scheduled LSF upgrades. Partner with an LSF specialist to encode scheduler expertise into templates and policies. The role also requires infrastructure automation, GitOps at scale, tooling development in Go, Python, and shell, and management of stateful production infrastructure.

The Staff Software Engineer will architect and implement scalable data systems, collaborate with teams, mentor engineers, and ensure data integrity.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Owns technical product marketing for Workday’s developer platform, creating credible demos, blogs, solution briefs, API narratives, documentation direction, launch assets, and keynote messaging. Translates complex platform and AI capabilities into developer-focused narratives, partners with Developer Relations, Product Management, Engineering, and Marketing, supports go-to-market strategy, and advises leadership on technical positioning and developer experience.

Build and operate developer experience and platform foundations across ephemeral environments, CI/CD, AWS infrastructure, infrastructure-as-code, feature flags, security scanning, observability, and AI-assisted development tooling. Create golden paths, improve build and test speed, consolidate pipelines, support multi-tenant HIPAA requirements, and carry pager responsibility for infrastructure used by engineering 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.

The Senior Platform Engineer at EDO will focus on enhancing internal platform capabilities using AWS services, Terraform, and improving team workflows to ensure reliable and efficient infrastructure.

Lead technical strategy and modernization of a high-scale communications platform. Improve reliability, observability, and operations across Kubernetes and multi-cloud environments. Drive migration planning, IaC adoption, incident response, architecture standards, and cross-functional engineering coordination while maintaining production stability.

Lead architecture and modernization of the Composer CMS platform, decoupling legacy dependencies while maintaining stability. Improve scalability, reliability, observability, and operational resilience for containerized, distributed production systems. Establish engineering standards, mentor engineers, run architecture reviews, and support cross-functional technical decisions using AI-assisted development tools.

Build integrations that discover, identify, and govern AI agents across third-party platforms such as Salesforce Agentforce, Microsoft, AWS, and Google Cloud. Retrieve native identity signals, integrate them with Delinea’s identity layer, and govern agents through provider APIs and attestation. Improve shared discovery capabilities while using connector patterns, OAuth, token exchange, and event-based ingestion. The role requires substantial backend engineering experience, strong integration skills, and familiarity with cloud, AI agent, security, or identity ecosystems.

Lead design, implementation, and operation of Signifyd's GCP/Kubernetes cloud platform. Architect scalable, secure infrastructure, embed AI-driven automation, own SLOs and incident response, improve developer experience via CI/CD/GitOps tooling, mentor engineers, manage cloud capacity/FinOps, and build observability and security into the platform.

Senior Software Engineer joining Fetch’s Data Platform team to architect and evolve scalable data pipelines, governed data access, delivery infrastructure, and partner integrations. The role owns ambiguous technical problems, leads multi-system initiatives and migrations, improves reliability and engineering efficiency, shapes technical direction, collaborates across teams, and mentors engineers. Candidates need strong backend or platform engineering experience, data infrastructure knowledge, cross-team leadership, operational judgment, and excellent communication skills.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.