[Hiring] Senior Software Engineer @AZX

Role 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. 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 (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.

Qualifications

• 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.
• Operated Kubernetes workloads that other people depended on — controllers or operators, scheduling, autoscaling, node lifecycle.
• Strong ability to threat-model isolation boundaries (namespaces, cgroups, seccomp, hypervisors).
• Hands-on experience deploying or operating open-weight LLM serving infrastructure (vLLM/SGLang or similar).
• Performance discipline in distributed systems: you measure before you optimize.
• Practical depth in some of our core stack — Rust (tokio), Python/FastAPI, Kubernetes operators (controller-runtime/Kubebuilder/CRDs), KEDA, Karpenter, GPU device plugins/DRA.
• Familiarity with isolation technology (Firecracker, Kata, gVisor, or comparable), secrets management and egress control (Vault/KMS-class).
• Comfort operating across cloud and GPU substrates — AWS/Azure/GCP plus managed GPU clouds.

Requirements

• 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.
• Please only apply to a maximum of 2 roles at a time; any applicants who apply to more than 2 roles within a 6 month period will automatically be disqualified.

Benefits

• Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.
• Competitive early-stage startup compensation (based on capabilities, experience, and location).
• Bonus eligibility.
• Health insurance with meaningful coverage for dependents.
• Flexible paid time off.
• Equity.
• Fully remote culture with a cluster of teammates in Seattle.

Next Steps

If this job sounds like a great fit but you don’t check ALL of these qualification boxes, we’d still love to hear from you!