Build and maintain infrastructure supporting large-scale ML training and inference workloads
Work with GPU and compute infrastructure, distributed systems, and cloud-native platforms
Improve reliability, observability, and performance across the platform layer
Collaborate directly with senior engineers and product teams on architecture decisions
Own production reliability — monitoring, incident response, and proactive risk reduction
Develop and maintain internal tooling and automation to support engineering operations

Requirements

5+ years of infrastructure or platform engineering experience in a production environment
Strong distributed systems background — experience with large-scale compute workloads preferred
Cloud-native infrastructure experience — AWS, GCP, or Azure; Docker and Kubernetes required
Familiarity with ML infrastructure a strong plus — training pipelines, inference serving, GPU workloads
Experience owning production reliability end to end

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

infrastructure engineeringplatform engineeringdistributed systemscloud-native infrastructureAWSGCPAzureDockerKubernetesML infrastructure

Soft Skills

collaborationincident responseproactive risk reductionmonitoringreliability improvementobservabilityperformance optimization