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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer (AI Inference & Runtime Platform) - **Company:** AZX INCORPORATED - **Location:** Seattle, WA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** OpenTofu, Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Software Debugging, Distributed Systems, Python (Programming Language), Key Management, Neo4j, Node.Js, Open Source Technology, Role-Based Access Control, Software Engineering, Datadog, Qdrant, Autoscaling, Large Language Models, Backend, Fastapi, Pgvector, Kubernetes, Bicep, SGLang, Model Inference, Terraform, Serverless Computing - **Published:** October 29, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-ai-inference-runtime-platform-seattle-wa--3437b501-4491-46bf-bd9c-c0ac2f93ddde ## About the Role Software Engineering, Artificial Intelligence (AI), Sustainability, Artificial Intelligence (AI), Physics, Logistics, Real Estate, Economics, Risk, Alternative Energy, * 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). * Bachelor's Degree; Master's is a plus ## Description You will be responsible for owning the layer where AI work physically happens: the machines, the isolation boundary, and the models running on them. This role anchors on two systems. The first is our inference control plane - open-weight models and custom task-model zoos, hosted and operated across managed GPU clouds and customer-managed Kubernetes clusters, with scale-to-zero economics, cold-start discipline, and per-token cost accounting that stays correct even when a client disconnects mid-stream - along with the Kubernetes layer those workloads live on: operators, autoscaling, node lifecycle. The second is our agent-sandboxing platform: hardware-isolated microVMs for running untrusted, agent-generated code securely and compliantly by construction, where agents operate with least privilege, never see a credential, and a human gates anything that writes to a system of record. You'll write Rust in the morning, a Kubernetes controller after lunch, and a FastAPI control-plane endpoint before you go home - building the fork engine, the guest agent, and the multi-substrate model lifecycle. We're looking for individuals who've built this class of stack (an inference-serving or serverless-GPU platform), operated it hard at scale, or ideally both. Responsibilities: * Manage the serving tier for open-weight models: engine deployment and configuration, 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 end to end with restore procedures that are regularly tested. * 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.