Infrastructure Engineer (Kubernetes Specialist)
typesafe ai is hiring an Infrastructure Engineer (Kubernetes Specialist) in San Francisco, United States. It pays $210k-$285k a year and Level rates it ; you can apply on Level.
AI in this role
We’re building prod, not god. TypeSafe is an AI lab pushing frontier model intelligence in a new direction, starting with System One Models. We combine the reasoning of LLMs with the reliability of code, for truly trustworthy AI. Our first model, Jev, launched in September 2026.
While others chase leaderboards, we’re rebuilding the LLM stack from first principles to make models smarter and also dependable enough to run in production. We’re a small, fast team backed by an $870M Series A led by a16z. Made in SF, with love. Come join the frontier.
About the roleWe're looking for an Infrastructure Engineer to build and operate the infrastructure behind TypeSafe AI's products at global scale. You'll own the systems that serve millions of users across regions — from provisioning Kubernetes clusters across multiple clouds to optimizing networking for low-latency AI inference.
This is a high-impact role on a small, fast-moving team. You'll work across the full infrastructure stack: cloud primitives, container orchestration, networking, observability, and the specialized infra that makes large-scale model inference efficient.
What you'll do
Design, deploy, and operate Kubernetes clusters across multiple regions and clouds
Build and maintain infrastructure for the platform that powers LLM inference workloads globally
Own networking, including VPCs, peering, load balancing, DNS, service mesh, CNI
Manage GPU infrastructure and autoscaling for ML workloads
Write and maintain infrastructure as code (Pulumi / Python)
Operate and improve observability: monitoring, alerting, tracing, logging
Requirements
Deep experience with Kubernetes in production at scale: networking, storage, scheduling, upgrades
Strong background in AWS
Hands-on experience with infrastructure as code (Pulumi, Terraform, or similar)
Solid understanding of Linux networking
Track record with high-traffic production ML systems
Programming fluency, Python preferred
Nice to have
Experience with large-scale LLM / ML inference infrastructure (GPU scheduling, model serving, vLLM, KubeRay, Kubernetes-native tooling)
Kubernetes networking depth with Cilium or other CNI plugins; service mesh (Istio, Envoy)
Multi-cloud infrastructure
Background in site reliability engineering including SLOs, incident response, capacity planning
We’re a small, flat, close-knit team working to make intelligence dependable enough to become part of everyday software. We work fully in person from our San Francisco office near Embarcadero station. We love what we do and care deeply about the work.
We strive for excellence and craftsmanship and won’t stop until we get there. When the team wins, we all win, and we enjoy collaborating and inspiring each other to grow—as a team and as individuals.
We value emotional honesty, kindness, and bringing your whole self to work. We build machines; we don’t try to be machines.
We want TypeSafe to be the place where you do the most impactful work of your career and help define our future as a company.
We provide100% covered health insurance
Daily lunch and dinner
Visa sponsorships
401K plans
How we rate this
Infrastructure Engineer (Kubernetes Specialist) at typesafe ai rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used vLLM hands-on?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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