Note: The job is a remote job and is open to candidates in USA. AZX is a mission-driven AI transformation company focused on critical industries, including climate and sustainability. The Senior Software Engineer will own AI inference infrastructure, Kubernetes-based workloads, GPU platforms, and secure agent-sandboxing systems, while building and operating control-plane services, stateful data stores, and infrastructure tooling.
Responsibilities
- Manage the serving tier for open-weight models: engine deployment and configuration (vLLM/SGLang), cold-start strategy, per-model SLOs, and upgrade/canary discipline
- Administer the Kubernetes layer for inference and sandbox workloads: operators and CRDs, autoscaling (KEDA/Karpenter-class), GPU scheduling and sharing, and node lifecycle
- Own the stateful data plane: hosted vector stores for semantic memory and graph stores for knowledge graphs — deployed, backed up, scaled, and recovered, with restores that are tested rather than hoped for
- Oversee the sandbox runtime and its host-side control plane: lifecycle, exec, snapshot/fork, teardown, metering, and the threat model of the isolation boundary
- Direct the FastAPI control-plane services, Terraform/OpenTofu, Bicep, and the dashboards
- Run the layer the backend services team builds on, expect to debug into their services, and expect them to read your dashboards
- Manage the open-source posture: build to OSS standards and release as it matures, with reviewed PRs, real docs, and reproducible builds
Skills
- 5+ years of shipping production systems in a systems language. Rust is the house language, but polyglots are welcome — deep Go, C/C++, or Zig with genuine appetite for Rust counts
- Async runtimes, memory-safety discipline, and debugging at the syscall boundary should be familiar territory
- Operated Kubernetes workloads that other people depended on — controllers or operators, scheduling, autoscaling, node lifecycle. You've been paged, and the experience changed how you build
- Strong ability to threat-model isolation boundaries (namespaces, cgroups, seccomp, hypervisors), including identifying what an untrusted guest could observe, forge, or exhaust, and applying security best practices for agentic execution — least privilege, no credentials in the sandbox, audit trails, and human approval on write actions
- Hands-on experience deploying or operating open-weight LLM serving infrastructure (vLLM/SGLang or similar), including packaging models into reliable, metered production endpoints
- Performance discipline in distributed systems: you measure before you optimize, and you can tell the story of a latency you killed with the numbers attached
- Practical depth in some of our core stack — Rust (tokio), Python/FastAPI, Kubernetes operators (controller-runtime/Kubebuilder/CRDs), KEDA, Karpenter, GPU device plugins/DRA — with a genuine willingness to research your way into the rest
- Familiarity with isolation technology (Firecracker, Kata, gVisor, or comparable), secrets management and egress control (Vault/KMS-class), and hosting stateful systems (vector stores like pgvector/Qdrant, graph stores like Neo4j) with backup and failover discipline
- Comfort operating across cloud and GPU substrates — AWS/Azure/GCP plus managed GPU clouds — using infrastructure-as-code (Terraform/OpenTofu, Bicep) and observability tooling (OpenTelemetry)
- Must be able to travel 2x/year for company summits
- Applicants must be currently authorized to work in the United States on a full-time basis
- We are unable to sponsor or take over sponsorship of employment visas at this time
Benefits
- Bonus eligibility
- Health insurance with meaningful coverage for dependents
- Flexible paid time off
- Equity
- Fully remote culture with a cluster of teammates in Seattle
Company Overview