Senior Quality Engineer

Senior Quality Engineer
Who we are

We’re doxy.me (doc-see-me), the world's most loved telemedicine solution, and we're on a mission to connect the world to the future of healthcare.

We believe that cost and complexity should never be a barrier to telemedicine. That’s why we created a simple, free, and secure telemedicine platform. Since 2013, we’ve grown to more than 1 million healthcare providers from 180+ countries who have clocked over 8 billion minutes of telemedicine sessions to date.

Our goal is to deliver the future of healthcare to every patient and healthcare professional on earth. Help us get there by joining our team of innovators, dreamers, and doers.

We’re a remote-first company with regional hubs for in-person collaboration (Austin, TX, Boston, MA, Charleston, SC, Salt Lake City, UT & London, UK).
Who you are

You own outcomes, not tasks. You can take an ambiguous, open-ended quality problem for a whole system and prioritise ruthlessly between risk, speed, and coverage without waiting to be told how.

You make testing everyone's job, not yours alone. You'd rather pair with an engineer on a risky test and make sure the right coverage exists than write it all yourself.

You look past the ticket. When a bug shows up, you're already asking what let it through, a gap in the AC, an untested layer, a missing gate - and fixing the class of problem, not just the instance.

You have genuine conviction about AI-augmented quality not as a buzzword, but because you've actually used AI-assisted tooling for test generation, selection, or maintenance, and you know where it currently earns its keep and where it doesn't.

You judge yourself on stability, escapes, and time-to-detect and never on test counts or coverage percentage. Those are diagnostics you use, not targets you chase.
What you’ll do

- Own quality strategy end-to-end for a system — lead testing strategy in close collaboration with Product and Engineering on acceptance criteria and test plans while a feature is still being shaped, not after it ships.

- Build the tooling that scales quality past headcount — design and extend shared test libraries, CI jobs, and agentic/AI-assisted testing infrastructure that other squads consume directly, kept fast and easy to adopt.

- Turn bug and incident data into systemic fixes — use QE data (bugs, coverage, flake, escapes) to spot patterns, not just individual defects, and follow them through to a fix that stops the whole class recurring.

- Coach, don't gatekeep — review and coach dev-authored tests at PR time rather than writing them in engineers' place, and drive automation in parallel with delivery, not as a follow-up ticket.

- Partner with product early — join acceptance-criteria conversations at refinement, contributing a risk lens that shapes what gets built before it's built.

- Report on outcomes, not activity — communicate quality in terms of stability, escapes, and time-to-detect, and push back when coverage percentage gets treated as the goal instead of a diagnostic.

- Push the AI-native bar forward — extend the team's agentic tooling, and know when a manual workflow is better handed to AI than kept as a hand-run check.

Your skills and experience

- Deep understanding of the surface you test and its integration points - coverage that's targeted, not exhaustive.

- Sound unit vs. integration vs. E2E judgement: you know which layer of the pyramid a risk belongs to, and you push back when a test is proposed at the wrong layer.

- Real experience building or extending shared test libraries, frameworks, or CI tooling that other teams rely on - not just using someone else's.

- Comfortable using and extending AI-assisted testing tools for real work, with an honest view of where current tooling falls short.

- Strong enough grasp of delivery data (stability, change-failure rate, escapes) to challenge stakeholders and prioritise with evidence, not opinion.

- Comfortable root-causing bugs in a large, unfamiliar co