Software Engineer – Cloud Infrastructure

FriendliAI•San Francisco, CA

About The Position

FriendliAI is looking for a Cloud Infrastructure Engineer to own the architecture and evolution of the cluster platform behind our GPU-accelerated AI inference cloud. As a Software Engineer, Cloud Infrastructure, you will design how our clusters are built and connected, extend Kubernetes where its defaults fall short, and own the network path that inference traffic depends on. Inference is an unforgiving workload for Kubernetes. Traffic is bursty and latency-sensitive, GPU capacity is scarce and inelastic, tenants must stay isolated, and multi-node serving depends on the network holding up under sustained load. This is a hands-on architecture role for an engineer who has already run large clusters in production and wants to push them further.

Requirements

5+ years designing, building, and operating large-scale Kubernetes infrastructure in production.
Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
Proven experience operating large-scale, high-traffic network services in production.
Deep understanding of Kubernetes internals: API server, scheduler, controller loops, kubelet, and etcd.
Strong command of Kubernetes and cloud networking: CNI, kube-proxy/eBPF datapaths, DNS, load balancing, service mesh, and VPC routing.
Proficiency with AWS, Terraform, Helm, and Ansible.
Programming skills in Go or Python, with the ability to build infrastructure tooling and automation.
Strong debugging skills across distributed systems, containers, and the Linux networking stack.
Clear written and verbal communication, including the ability to document architectural decisions for other engineers.

Nice To Haves

Large-scale Kubernetes operations in a high-traffic domain such as gaming, e-commerce, or public cloud.
Cilium and eBPF, including kube-proxy replacement or upstream contributions.
Cluster provisioning and lifecycle management with Kubespray or similar Ansible-based tooling.
GPU orchestration: NVIDIA GPU Operator, device plugins, or Dynamic Resource Allocation (DRA).
High-performance networking for distributed workloads: RDMA/RoCE, InfiniBand, EFA, SR-IOV, or NCCL tuning.
Multi-cloud, hybrid-cloud, or bare-metal Kubernetes operations.
Contributions to Kubernetes, Cilium, Istio, or other CNCF projects.

Responsibilities

Own the architecture of our multi-cluster, multi-tenant Kubernetes fleet across both managed and self-managed clusters: cluster topology, control plane and etcd lifecycle, and zero-downtime upgrades.
Extend Kubernetes with custom controllers, operators, and CRDs so platform behavior is encoded in software rather than runbooks.
Design GPU scheduling and capacity strategy, including topology-aware placement, node pools, priority and preemption, and quota across tenants.
Build autoscaling that matches inference traffic: queue-driven pod scaling, node autoscaling, scale-to-zero, and cold-start reduction.
Own the Kubernetes network data plane: CNI, IPAM, DNS, ingress, and L4/L7 load balancing.
Design cross-AZ, cross-region, and cross-cluster connectivity, and operate the service mesh for routing, mTLS, and traffic policy.
Debug production network issues (packet loss, conntrack exhaustion, MTU mismatches, DNS latency, load balancer behavior) and drive permanent fixes.
Define SLOs for platform-critical systems and lead post-incident hardening.
Deliver infrastructure as code with Terraform, Helm, and GitOps.
Partner with the inference engine, platform, SRE, and security teams to turn serving requirements into platform capabilities.