About Wander

Wander is revolutionizing the $100 B+ short‑term rental industry. We curate every aspect of the guest experience—from our smart homes to our intuitive booking platform. Our mission is to help people find their happy place, and our vision is to create the infrastructure for people to experience the world.

Backed by top‑tier investors and led by experienced startup veterans, we're a tight‑knit, remote‑first team united by our passion for travel, technology, and creating unforgettable experiences.

Staff Software Engineer in Test (SDET)

Role Overview

As a Staff Software Engineer in Test, you will own the architectural vision, strategy, and infrastructure for quality, testability, and engineering velocity across our entire engineering organization. You are not just writing test cases; you are building the platforms, tools, and guardrails that let product teams ship high-quality code at lightning speed.

This role exists at a specific moment. We ship fast, and a growing share of our code is written with AI assistance, by more contributors, more often. That velocity is an advantage we intend to keep. It also means more change hitting the pipeline per day than any manual review process can hold. Your job is to build the automated verification, testability infrastructure, and guardrails that make high-velocity, AI-assisted development safe rather than reckless. You are how we convert "fast because we're reactive" into "fast because the system catches what we miss."

You will work closely with engineering leadership, infrastructure teams, and product engineering to shift testing left, eliminate flaky tests, and build quality engineering into our CI/CD pipelines as a first-class concern.

Core Responsibilities

Architecture & Tooling Innovation

• Design & scale frameworks. Architect, build, and maintain robust, scalable automation frameworks for API, UI, mobile, and backend microservices.
• Developer experience (DX). Create internal tools, CLI utilities, and mock services that make it frictionless for product engineers, and the AI tools they work alongside, to write and run reliable tests.
• CI/CD integration. Own the integration of automated testing, linting, security scanning, and performance testing into our deployment pipelines for safe, continuous delivery.
• Verification for AI-assisted code. Build the test generation, coverage analysis, and pre-merge checks that let the org adopt AI coding tools aggressively without lowering the floor on quality. The faster code is produced, the more the safety net has to be automatic.

Quality Strategy & Governance

• Shift-left leadership. Champion a culture where quality is a shared responsibility from the first day of design, not an afterthought.
• Metrics & observability. Establish KPIs for system reliability, test coverage, and pipeline health. Build dashboards that track and reduce flaky tests and deployment bottlenecks, and that distinguish velocity from churn so we can see whether speed reflects good planning or reactive firefighting.
• Advanced testing. Design infrastructure for advanced methodologies including chaos engineering, load and performance testing, and synthetic data generation.

Leadership & Influence

• Technical mentorship. Guide and mentor engineers across the organization on testing best practices, testability design patterns, clean code, and how to review and harden AI-generated code rather than rubber-stamp it.
• Cross-functional alignment. Collaborate with Principal and Staff Engineers across Infrastructure and Product to ensure new architectures are inherently testable and observable.

Technical Profile

Required Expertise

• Systems programming. Advanced proficiency in modern backend languages (e.g., Python, Go, Node.js/TypeScript, Java).
• Automation frameworks. Deep experience with modern testing tools and libraries (e.g., Playwright, Cypress, Appium, PyTest, or native integration test libraries).
• Cloud & infrastructure. Hands-on experience with cloud infrastructure (AWS/GCP), containerization (Docker, Kubernetes), and CI/CD platforms (GitHub Actions, GitLab CI, or CircleCI).
• Databases & microservices. Solid understanding of testing distributed systems, asynchronous event-driven architectures, and profiling database queries.

Valued

• Experience building quality tooling for teams using AI coding assistants at scale: test scaffolding, coverage gating, automated review, and the guardrails that keep a high-velocity pipeline honest. You do not need to have done all of this; you do need to be the kind of engineer who would build it.

Qualifications

• Experience. 8+ years in software engineering, with at least 3+ years operating at a Senior, Lead, or Staff level focused on automation infrastructure and tooling.
• Mindset. A product-engineering mindset. You treat the internal engineering team as your primary customer, focused on improving their velocity and their confidence.
• Communication. Exceptional ability to articulate complex technical strategy and to influence engineering culture across multiple teams.

What Success Looks Like in 12 Months

• Deployment velocity that's real, not reactive. Time-to-production stays fast because automated feedback loops are fast, reliable, and trusted, not because we're skipping steps.
• Flake zero. Flaky tests are automatically quarantined or eliminated, restoring engineering trust in the pipeline.
• Self-serve quality. Product teams confidently write and deploy their own integration tests on the infrastructure you built.
• Safe AI velocity. AI-assisted code ships at high volume with coverage and pre-merge verification that hold the quality line automatically, so adopting AI tools raises throughput without raising defect or revert rates.