Devops Engineer
DevOps Engineer at Menlo responsible for building and maintaining the infrastructure that powers inference serving, training rigs, and agentic coding infrastructure, leveraging Kubernetes, Docker, CI/CD pipelines, AWS, and Terraform to ensure reliable, scalable operations for the Asimov humanoid robot platform.
About Menlo
Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable -- turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
The Role
As an DevOps Engineer, you will own and evolve the platform that everything at Menlo runs on -- from inference serving, to training rigs, to the agentic coding infrastructure that powers day-to-day engineering. You will work deep in the stack across Kubernetes, networking, and where it matters bare metal, and help set the technical direction for how Menlo Cloud scales.
What You'll Do
Operate and evolve our Kubernetes platform across multiple clusters and environments (Prod, Dev, hybrid on-prem and public cloud), covering control plane operations, node lifecycle, upgrades, and autoscaling at every layer (Cluster Autoscaler, HPA, KEDA).
Architect and manage hybrid cloud infrastructure spanning on-premises and public clouds (GCP, AWS), including workload placement, cross-cloud networking, and unified resource management.
Own the CI/CD and GitOps experience end-to-end: container build pipelines, image optimization, and progressive delivery via ArgoCD / FluxCD.
Own the observability stack as a single pane of glass across all clusters: Grafana, Mimir, Tempo, Loki, Pyroscope, OnCall, Prometheus -- and help push toward agent-assisted SRE workflows.
Manage and improve our inference platform: vLLM serving and AIBrix for multi-model orchestration and autoscaling across a fleet of NVIDIA GPUs.
Operate platform services: Kafka, Redis, PostgreSQL, OpenSearch.
Manage identity and access via Keycloak integrated with Google Workspace; harden SSO, RBAC, and secrets management across the platform.
Harden network security across private load balancers, firewalls, and VPC segmentation; design and maintain hub-and-spoke / multi-AZ topologies.
Support training infrastructure: self-service VM provisioning, RunPod burst capacity, Weights and Biases integration.
Drive infrastructure reliability, cost efficiency, and capacity planning as the platform scales.
What We're Looking For
Kubernetes -- deep, hands-on. Strong production experience with Kubernetes, fluent in workloads and controllers, networking (Services, Ingress, CNI basics), storage (PV/PVC, CSI), RBAC, and the autoscaling story end-to-end (HPA, VPA, Cluster Autoscaler, KEDA). Cloud-managed Kubernetes (GKE, EKS, AKS) is fine; on-premises / self-managed Kubernetes (kubeadm, Cluster API, k3s, etc.) is a strong plus.
Networking -- de
Posted June 18, 2026