About the role
Level: Senior-level (5+ years experience) Location: SF Bay Area About Runta Runta builds runtime infrastructure that lets AI agents execute reliably over long time horizons: run agents efficiently, govern what they can reach, and record what they did. Workloads that run for hours or days break the assumptions web infrastructure is built on. An agent can fan out fifty sub-tasks in a second and then go idle for ten minutes, must survive instance recycling mid-run, and needs to snapshot, resume, and branch its execution state the way developers branch code. We’re building the substrate that makes this possible. We’re a small, well-funded team, moving fast.
The Role Our runtime team builds the execution engine. You build the cloud platform it runs on: the production infrastructure that serves paying customers. You are the person accountable for it working, scaling, and not bankrupting us. Runta does three things: run, govern, record. This role makes run economical at scale, gives record a storage layer that’s cheap at volume and fast on restore, and owns the network boundaries that make govern enforceable in production. You decide ECS vs. EKS, when Spot instances make sense, how snapshot storage should be tiered, and what the architecture costs at 10x load. You’ll also own the infrastructure-as-code layer that makes our platform reproducible and portable. Writing Terraform is table stakes here. The job is designing modules other engineers consume, managing state at scale, and building abstractions that keep multi-cloud on the table without over-engineering for it today. This is an ownership role, not a support role. The infrastructure layer is part of the product: customer-facing, versioned, reproducible. What You’ll Do Design and operate Runta’s production infrastructure on AWS: compute, networking, storage, observability Own the IaC layer: Terraform modules, state management, CI/CD for infrastructure changes Design cross-cloud abstractions that don’t lock us into a single provider Own the infrastructure side of snapshot/resume/branch: snapshot graph storage, control plane / data plane separation, local ↔ cloud state consistency Make cost-aware architecture decisions: instance strategy, Spot fleet design, storage tiering, rightsizing Shape the infrastructure roadmap as an early technical leader, working directly with the founder The Problems You’d Be Solving A workload runs anywhere from 2 seconds to 2 hours, unpredictably. How do you size, schedule, and price the fleet under it? Agents checkpoint constantly. Snapshot storage must be cheap at petabyte volume but restore in seconds. What’s the tiering design? An agent job fans out 50 concurrent sub-tasks, then the whole fleet goes idle for 10 minutes. Autoscaling policies tuned for web traffic fall over. What replaces them? Agents retry non-deterministically on failure. Where must the infrastructure itself be idempotent, and where is eventual consistency a silent-corruption risk? The same platform needs to deploy identically across environments, and someday across clouds. What do you abstract, and what stays provider-specific? If you read those and started sketching answers, we want to talk to you. What We’re Looking For You’ve designed and operated production AWS infrastructure you were accountable for, not just worked within someone else’s Deep Terraform (or equivalent IaC) experience: complex modules built for other engineers to consume, state management at scale, infra CI/CD Multi-cloud or cross-cloud experience: provider-spanning abstractions, workload migrations, or internal platforms built on IaC Experience running serverless or container platforms at scale (Lambda, Fargate, ECS, EKS) A cost-optimization track record you can talk through in specifics: RI strategy, Spot fleet design, rightsizing with real numbers Solid distributed systems fundamentals: consistency models, replication, consensus You understand orchestration platforms at the why level, not just the kubectl level
