# Software Engineer, CI Platform Infrastructure

**Company:** [Airbnb](https://hotfix.jobs/companies/airbnb)
**Location:** Remote
**Role:** DevOps / SRE
**Salary:** $162k – $190k/yr
**Skills:** Kubernetes, EC2, Go, Docker, Distributed Systems
**Posted:** 2026-07-21

> Build and optimize a next-generation CI platform infrastructure for workflow orchestration, scheduling, caching, and autoscaling to accelerate software development for engineers and AI coding agents at scale. Requires interest in distributed systems and knowledge of Kubernetes, EC2, Golang, and Docker.

## Job Description

## Responsibilities
- Experiment with and prototype new features in a remote sandbox development environment to enable new capabilities in CI-related workflows.
- Debug workflow orchestration queueing delays and measure the performance of remote execution/cache infrastructure to improve correctness, reproducibility, and speed.
- Identify root causes of infrastructure-related reasons for test flakiness in CI.
- Optimize the speed of autoscaling CI workers to handle bursts in CI workloads from product engineering teams.
- Up-level observability and alerting of the CI platform across build systems, flaky test management systems, and the merge queue.
- Build an improved canarying mechanism to roll out workflow orchestration changes iteratively and safely.
- Design and build key pieces of a new CI platform for workflow orchestration, focusing on scheduling, queueing, backpressure, consistency, caching, fault tolerance, and observability.
- Work on scheduling work onto the most cost-efficient compute stack.

## Requirements
- Bachelor’s and/or Master’s degree, preferably in CS, or equivalent experience.
- Interest in working on distributed systems engineering and optimization problems.
- General knowledge of most of the following: Kubernetes, EC2, Golang, Docker.
- Metrics-driven approach to decision-making, with an interest in delivering quantifiable impact.

## Nice-to-Haves
- Experience with CI platforms, build systems, or workflow orchestration at scale.
- Background in optimizing for AI-scale software development workloads.

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