QA Lead

The Art of Living is working toward something unfashionably large: a happier, safer world, built on real human values. Most people meet that mission through its programs and volunteers. Increasingly, they meet it through software — and the technology team is the group that builds it, creating products that help the Foundation reach millions of users across the globe.

We're a small, product-focused team with a grand vision. At our scale, quality can't be a department at the end of the pipeline — it has to be a discipline every team owns, and this role exists to make that true in practice, not just in principle. We value collaboration, continuous learning, and real-world impact, and we look for people who are genuinely excited to take on challenging problems in a supportive environment.

We're looking for a QA Lead to own quality across everything we build — web and mobile apps, the services and APIs behind them, how fast they run, and the data that flows through them. You'll start as our quality owner with no team of your own, wearing several hats and working directly with engineering leads, product leads, and stakeholders — and you'll help build a QA team as the work demands. It's a hands-on role for someone who has seen enough testing fashions come and go to know which practices actually protect users.

AI has changed how software gets built here, as it has everywhere. Code arrives faster, a test suite can be generated in minutes, and a green dashboard is easier to produce than ever. None of that tells you whether the product works for the person using it. Generated tests tend to assert whatever the code already does, bugs included, and a self-healing test can heal its way straight past a real regression. A suite like that gives a team confidence it hasn't earned.

So we want a lead with a level-headed, common-sense view of the craft. Use AI wherever it genuinely saves time — drafting test ideas, generating test data, reading an unfamiliar codebase, triaging a wall of failures — and check its work the way you'd check a new colleague's. Automate what's stable and repetitive, explore what's new and risky, and be able to explain which is which. Coverage is a number; confidence is a judgment — and the judgment is the job.

You'll own quality across all of our teams, starting with strategy: understanding what each product does, who depends on it, and where it's most likely to fail — then deciding what to test, at which layer, how deeply, and what not to test at all. You'll set the bar a release has to clear, own the go/no-go signal, and make the call — ship, hold, or roll back — with the evidence to back it.

The work is hands-on and deliberately broad. In a single week you might explore a new mobile flow on a mid-range Android phone over a patchy connection, tighten the automated regression suite ahead of a web release, and probe an API for the failure modes nobody wrote down — malformed input, expired tokens, retries, timeouts, and partial failures between services. You'll test across the browsers, devices, and languages our users actually have, automate what's stable at the layer where it's cheapest to check, keep the suites fast and trustworthy, and treat a flaky test as a bug rather than background noise.

Performance is part of quality, not a separate project. You'll set budgets for how quickly pages and app screens load and how fast our APIs respond — in percentiles, never averages — and measure against them both in the lab and in the field, with real users. You'll design and run load tests before big launches and the traffic spikes that follow them, and catch performance regressions in CI before anyone feels them.

Quality doesn't stop at the screen or the API. You'll validate the data pipelines behind our products — checking that ETL jobs deliver complete, accurate, and timely data, that source and target reconcile, and that a schema change upstream doesn't quietly break a report downstream. You'll make sure analytics events fire when and how the tracking plan says they should, so that product decisions rest on numbers people can trust.

Production is the final test environment, whether we like it or not. You'll use our observability stack — logs, metrics, traces, error tracking, and synthetic checks — to see what testing missed and how real users experience each release. When a bug escapes, you'll be part of the incident response and lead the root-cause analysis — and every incident should leave something behind that catches it next time: a new test, a better alert, or a sharper question in the next design review.

Much of the job happens in conversation. With engineering leads, you'll shape testability, CI quality gates, and release readiness; with product leads, you'll sharpen acceptance criteria and surface risk before a line of code is written; and with stakeholders across the organization, you'll explain in plain language what's safe to ship, what isn't, and why. When something isn't ready, you'll say so clearly — and bring a way forward.

You'll also set how we use AI in testing: where it saves real time, where its output needs a second pair of eyes, and where it doesn't belong. Around that, you'll build the practice itself — test data and environments, a bug-triage rhythm teams actually follow, and a handful of quality measures that track what users experience, such as escaped defects and incident trends, rather than how many tests ran. As the work grows, you'll help hire and mentor the QA engineers who join you.

You have eight or more years in QA, testing, or quality engineering on web and mobile products, including time as the person accountable for quality — setting the strategy, not only executing it. You've tested systems at real scale, in production, and you've been through enough releases and incidents to know the difference between a process that protects users and one that only produces paperwork.

Your range is the point. You're hands-on with browser and mobile automation — Playwright, Cypress, Selenium, Appium, Maestro, or similar — and fluent in API testing, from Postman collections to code-based suites that cover auth, contracts, and failure modes. You can design a load test in k6, JMeter, or Gatling and read the results in percentiles, and you know your way around Core Web Vitals and Lighthouse on the web, and app-start time, slow and frozen frames, and ANRs on mobile.

You write real SQL — enough to validate a pipeline and reconcile two data sets — you can check analytics events against a tracking plan, and you're at home in observability tools such as Grafana, Datadog, ELK, or Sentry. You code in TypeScript, Python, or similar well enough that developers review your tests like any other code. And you use AI tools in your own testing work, with a clear, experience-based view of where they help and where they mislead.

Just as important is how you work with people. You can explain risk to an engineer, a product lead, and a non-technical stakeholder in terms each of them cares about. You push back without drama, you influence without authority, and you're comfortable being the only quality specialist in the room.

Extra credit for testing AI-powered features (golden datasets and evals, non-deterministic output, prompt injection), mutation testing to prove your tests actually catch bugs, security-testing fundamentals (the OWASP Top 10, ZAP or Burp Suite), accessibility testing against WCAG, contract testing (Pact or similar), and data-quality tooling such as dbt tests or Great Expectations. Having built a QA function from scratch will make us lean in, as will time spent on payment or donation flows, multilingual products, or other high-scale consumer apps.

Hybrid working, a supportive and mission-driven environment, and the chance to shape how an entire organization thinks about quality — for technology that reaches millions of people and does real good in the world.

Titles aside, here's the shape of the person we're looking for. You're level-headed: you can tell a real risk from noise, and you spend testing effort where a failure would hurt users most. You're at home anywhere in the stack — a mobile screen, an API contract, a load test, an ETL job, a dashboard — and you'll follow a problem across all of them until you find its cause. You treat AI as a fast collaborator whose work you always check. And you can sit down with an engineering lead, a product lead, or a stakeholder, explain what's safe to ship and why, and be trusted when you say it isn't ready yet.