Member of Technical Staff, Infrastructure

Infrastructure Engineer About Obvious We're building an AI-native workspace—an operating system for work that puts co-intelligence at the center. Start with data or an idea, describe your goal, and Obvious goes to work: running analysis, searching the web, writing documents, generating tables, designing presentations, visualizing data, building dashboards, and more. As Steve Jobs imagined the personal computer as a bicycle for the mind, Obvious imagines AI as a garden for the mind. Less mechanical acceleration. More organic cultivation. What if, instead of just vibe coding, you could vibe-work? What if getting from idea to done wasn't so opaque, stubborn, and high-latency? What if there was a way to consistently deliver work that feels like it came from the best version of you on your best day? That's Obvious. Why we're hiring for this role We're not looking for the traditional IT-professional profile—someone who knows Linux, box configuration, and enterprise DevOps but hasn't rethought infrastructure for the AI era. We're looking for an engineer who has spent their career treating infrastructure as the product itself, and who brings that lens to building AI-native infrastructure tooling. That means owning the systems that make every Obvious engineer, and every Obvious agent, more productive: build and deploy pipelines where rolling back and forth is trivial, model-serving and inference infrastructure that holds up under real AI workloads, and the observability to know what's actually happening in a system that's non-deterministic by nature. We are small and talent-dense. Among our founding team, we have world-class builders, former founders, and leaders from companies like Netflix, Google, Uber, Meta, Dropbox, Instacart, Shopify, Apple, Datadog, and Twitter (X). If you're excited to build infrastructure that enables others—human and agent—to do their best work, join us. In this role you will: Make deployments boring (in the best way possible) Own CI/CD pipelines: optimize build times, improve caching, reduce flakiness Evolve our Kubernetes (EKS) deployment strategy for reliability and speed Build and harden the infrastructure behind model serving, inference, and agent tooling—not just the app layer around them Extend our telemetry with better instrumentation, smarter sampling, and actionable dashboards, including eval pipelines and LLM-ops guardrails Build alerting that catches actual problems and ignores noise Make the feedback loop from code to production as fast as possible Improve preview environments, local dev tooling, and testing infrastructure Eliminate toil through thoughtful automation, not another dashboard nobody reads Be the engineer who makes other engineers—and agents—faster You will thrive in this role if you have: Come from a company where infrastructure was the product itself—not infrastructure work done in service of someone else's product. Think platforms like Vercel, Railway, Fly.io , Render, Heroku, Netlify, Supabase, Modal, or similar—ideally as an early hire or in a role with real ownership Applied that infra background specifically to AI/LLM workloads: model serving, inference infrastructure, agent tooling, eval pipelines, or LLM-ops guardrails—working at an "AI company" alone doesn't count if the infra work itself doesn't show this lens Real technical depth in distributed systems: Rust or Go, storage engines, control planes, Ceph, RDMA, eBPF, bare-metal automation, or Kubernetes internals (not just usage) A proven record of exceptional achievements and impact Strong Terraform skills—you've managed real infrastructure as code Hands-on experience with observability tools: OpenTelemetry, Datadog, Dash0, Braintrust, distributed tracing, metrics, structured logging You've been on-call, and you've built systems that made on-call better You think like a product manager for internal tools, where the product is developer (and agent) productivity Willingness to work hard, move fast, and