Senior Site Reliability Engineer
TLDR
Work at the core of large-scale simulation for autonomous systems, collaborating closely with engineering teams to enhance world-class cloud infrastructure.
Infrastructure ownership and cloud operations. Design, build, and maintain multi-region AWS infrastructure using Terraform. Operate and scale EKS clusters across production regions: autoscaling, node lifecycle, workload health. Manage networking across environments: VPC design, DNS, load balancing, and cross-region connectivity. Support infrastructure changes, migrations, and expansions into new regions. Contribute to and improve GitOps-based deployment workflows using GitHub Actions, Helm, and Kustomize.
Reliability engineering and incident response. Help build and run incident management processes: severity definitions, escalation paths, on-call practices. Lead incident response, debugging, and root-cause analysis. Write postmortems and drive systemic reliability improvements from what they surface. Improve observability across metrics, logging, tracing, and dashboards. Support GPU and batch workloads running on Kubernetes.
Security and access management. Provide security-conscious feedback on platform architecture decisions. Own cloud IAM governance: roles, policies, and access boundaries across accounts and services. Lead compliance-adjacent work including audit-readiness, partner certification requirements, and supporting responses to customer security questionnaires.
Experience. 5+ years in SRE, DevOps, or infrastructure engineering roles, with a track record of operating production systems across multiple regions.
Terraform. Modules, state management, and multi-environment patterns.
AWS depth. Solid experience across VPC, IAM, EKS, S3, and CloudWatch.
Kubernetes expertise. Cluster operations, autoscaling, RBAC, and Helm.
CI/CD and GitOps. Experience with GitHub Actions, ArgoCD, or similar workflows.
Networking fundamentals. CIDR, DNS, load balancing, VPN, and cross-region connectivity.
Observability. Experience with tooling such as Prometheus and Grafana.
Scripting. Comfort with Python and Bash for tooling and automation.
Cross-platform familiarity. Working knowledge of both Linux and Windows environments. Operational experience supporting Windows-based workloads is a meaningful advantage.
Windows on Kubernetes. Experience with Windows node pools, Windows AMIs, and GPU-adjacent components on K8s.
GPU scheduling. Familiarity with GPU scheduling on Kubernetes, including NVIDIA device plugin configuration.
Domain workloads. Experience supporting simulation, ML, or rendering workloads in cloud infrastructure.
AWS extras. Exposure to AWS Storage Gateway, Active Directory integrations, or AWS Transfer Family.
Service mesh. Familiarity with service proxy or service mesh patterns.
Container OS. Experience with container-optimized OS images (e.g., Bottlerocket, Packer).
Parallel Domain creates a robust platform for testing and validating autonomous systems and Physical AI through high-fidelity virtual simulations. This service is designed for developers and companies in the AI and robotics sectors, enabling them to push their innovations to the brink in a controlled environment.
- Employees
- 11-50 employees
- Total raised
- $2.5M raised