The opportunity
Iterable is the leading AI-powered customer engagement platform that helps leading brands like Redfin, SeatGeek, Priceline, Calm, and Box create dynamic, individualized experiences at scale. Our platform empowers organizations to activate customer data, design seamless…
What you'll do
Own the developer delivery path end to end: Improve the systems engineers' use to build, test, package, deploy, and validate changes with confidence across local development, CI, and production rollout workflows.
Lead shared engineering foundations: Evolve common libraries, build patterns, service templates, internal platform capabilities, and paved-path workflows that many teams depend on.
Improve CI performance, capacity, and reliability: Drive improvements to build and test feedback loops, runner health, workload placement, and failure isolation so engineers get faster, more predictable results.
Strengthen deployment and release infrastructure: Own and improve the control-plane systems that underpin delivery, including GitOps workflows, rollout orchestration, self-service deployment tooling, and the operational paths used to debug and recover production changes.
Take ownership of platform runtime infrastructure: Help operate and improve the Kubernetes and cloud infrastructure that underpins the engineering workflow, including cluster behavior, node-pool strategy, workload scheduling, access patterns, and reliability guardrails.
Turn recurring friction into durable leverage: Diagnose repeated failure modes across build, CI, deploy, and runtime systems, then solve them through automation, better defaults, observability, documentation, and platform product improvements.
What they're looking for
- Strong software engineering fundamentals in Scala, Java, Kotlin, or another: production language; able to read, modify, test, and review service code in a large shared codebase.
- Experience owning a developer platform or infrastructure systems, such as: build platforms, CI/CD, deployment tooling, internal developer portals, Kubernetes platforms, or cloud runtime infrastructure.
- Strong working knowledge of build systems and delivery workflows; familiarity: with Kubernetes, GitHub Actions, ArgoCD, Backstage, Bazel, EngFlow
- Ability to debug complex failures that cross boundaries between application: code, build tooling, CI runners, deployment systems, Kubernetes, and cloud infrastructure.
- Experience leading migrations or foundational platform changes with a: meaningful blast radius, including rollout planning, risk management, communication, and follow-through on adoption.
- Product-minded approach to internal platform engineering; you care about: usability, feedback loops, self-service, operational clarity, and whether platform investments make engineers faster and more confident.
- Comfort using AI tools such as Cursor, Claude, or similar systems for real: engineering work, including codebase exploration, migration support, test generation, CI triage, and agent orchestration.
- Good judgment about AI-assisted automation: able to break complex tasks into plans, prompts, verification steps, and review loops with appropriate human oversight and safety guardrails.
