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Senior/Staff Platform Engineer

Coderabbit
CompanyCoderabbit
CategoryEngineering
LocationSan Francisco
RemoteHybrid
EmploymentNot stated
LevelSenior
SalaryUSD 220k–280k
Posted17 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
ABOUT CODERABBIT CodeRabbit is an innovative research and development company focused on building extraordinarily productive human-machine collaboration systems. Our primary goal is to create the next generation of Gen AI-driven code reviewers: a symbiotic partnership between humans and advanced algorithms that significantly outperforms individual engineers. We combine language models with human ingenuity to push the boundaries of software development efficiency and quality. About the Role CodeRabbit is hiring its founding Platform Engineer. You'll own the compute, orchestration, and infrastructure layer that CodeRabbit's AI engine and every product on top of it run on, across a multi-region GCP footprint and a product suite that's still growing. This is a 0-to-1 role: you'll build the platform function from the ground up, set the technical direction, establish the patterns everyone else builds on, and make the early architectural calls that are expensive to unwind later. This role carries the same ownership and pace as the rest of CodeRabbit engineering, applied with the rigor that foundational infrastructure demands. The systems you build here need to be right, not just fast — everything else depends on them. Required Qualifications - 7+ years in Platform Engineering, Infrastructure Engineering, or Site Reliability Engineering with a strong bias toward building platforms, not just operating them - Deep, hands-on experience with Kubernetes: you've gone beyond running workloads to understanding and configuring the control plane, writing operators or controllers, tuning schedulers, and debugging at the runtime level; bonus if you've contributed to Kubernetes or built on its internals - Experience building and running large-scale distributed systems. You understand the trade-offs between consistency and availability, have debugged distributed failures in production, and have designed systems around them - Strong cloud compute background on GCP or AWS. You've built infrastructure, not just consumed managed services; you understand how compute, networking, and storage primitives work at the layer below the console - Proficiency in Docker and container runtime internals: image layering, networking modes, security contexts, and build optimization Technical Skills - Container & Orchestration: CRDs, operators, admission webhooks, RBAC, network policies, autoscaling; strong Docker/OCI toolchain knowledge - Distributed Systems: queuing, eventual consistency, backpressure, graceful degradation - Infrastructure as Code: Advanced Terraform — module design, state management, programmatic provisioning patterns - Cloud Platforms: GCP (GKE, Cloud Run, VPC, IAM, Cloud SQL, Cloud Storage, Load Balancing) — how these work under the hood, not just how to configure them - Programming: Node.js/TypeScript or Go for platform tooling, operators, and automation - Observability: Datadog, Prometheus/Grafana or equivalent — custom instrumentation, distributed tracing, SLO-based alerting - Systems: Linux internals, networking fundamentals (TCP/IP, DNS, load balancing, eBPF), storage systems Why Join Our Engineering Culture? - CodeRabbit is building the next generation of AI-native developer tooling; starting with code review. We combine large language models with deep software engineering context to help teams ship faster, catch more bugs, and make better architectural decisions at scale. - We have a high-ownership engineering culture. That means no passive execution, no waiting for perfect tickets, and no narrowly defined task boundaries. Engineers here find problems before they're assigned, use AI as a core part of how they build, ship with judgment, and own outcomes from proposal to production. - Our operating philosophy: bias toward action, ship the smallest necessary coherent slice, validate proportional to risk, watch what happens, and make the system better. AI drafts; humans decide. Speed matters,
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