About us
As the official pet registration provider for more than 250 jurisdictions, DocuPet is the largest and fastest growing pet registration platform in North America.
Our proprietary platform consolidates all pet information into a single place and provides the services for pet owners, community members and animal shelters to ensure pets can be reunited quickly if they become lost.
Beyond our platform, DocuPet offers specialized pet tags, an AI-powered pet tracker, lost pet alert system, and will soon be launching a first-of-its-kind pet parenting mobile app, all aimed to ensure every pet in North America is registered and that each has a safe and happy home.
Our work is very important. More than 6 million pets enter animal shelters every year. Just 10% of those animals are returned to their owners. Effective registration, pet identification, reunification tools, and animal shelter resources, all provided by DocuPet, is the solution that will measurably reduce shelter intakes while providing significant new funding for animal welfare organizations.
About the role
We're hiring a Staff Software Engineer, Quality to be the technical anchor of DocuPet's QA automation platform. You'll report to the Director of QA and set the software & quality engineering direction for how we test.
Most of our QA team is moving from manual regression, functional, and exploratory testing into automation, growing into full Software Engineers, Quality. You're the person who makes that transition real. You design and build the Playwright-based platform they work in, set the patterns they inherit, and coach them from their first fixture to owning a suite end to end.
We operate in an AI-first environment. AI already generates most of the code we deploy, and the SDLC itself is moving toward agentic workflows. Test generation is the next frontier. We're building toward AI generating functional and regression coverage from engineering and product specs, and your job is to build the system that makes those AI-generated tests trustworthy: the spec-to-suite pipeline, the shared fixtures and contract harnesses, the seed data strategy, and the prompt and context patterns the team uses every day. Weak foundations produce weak AI output, so this work sets the ceiling for everything built on top of it.
This role also sits close to leadership. You'll partner with and advise software and quality engineering leadership on automation strategy and AI-first practice, and you'll be in architectural and roadmap conversations early enough to make testability a design input rather than a late fix. You lead with craft, you earn trust through consistency, and you leave every system you touch more reliable than you found it.
What you'll do
- Own the architecture and build of DocuPet's Playwright-based test automation platform, and set the patterns, fixtures, and abstractions the QA team will build on for years
- Design the AI-first testing infrastructure the team depends on: the spec-to-suite pipeline, shared fixture libraries, API contract harnesses, seed data strategy, and the prompt and context patterns used to generate tests
- Set the bar for what good looks like when engineers evaluate AI-generated tests, from assertion depth and edge cases through flake risk and coverage
- Own automated accessibility testing as a first-class part of quality: build WCAG 2.2 AA checks into the Playwright platform and enforce them as a CI gate on every pull request
- Coach the QA team through their move into automation. Review their work, grow their AI-first testing skills, and build the shared docs, libraries, and patterns that make good practice repeatable instead of dependent on one person
- Partner with and advise software and quality engineering leadership on automation and AI-first testing strategy
- Get into architectural and roadmap planning early. Push back on ambiguous specs, surface systemic quality risks such as undertested interfaces and coverage debt, and bring structural solutions rather than reactive fixes
- Treat quality metrics as a feedback system. Use defect escape rate, flake rate, AI output tweak rate, and coverage gaps to improve prompts, specs, and infrastructure, and set the reporting rhythm that keeps leadership informed
- Bring strong SQL to the data and pipeline layer, where AI-generated tests alone won't reliably cover transformations, lineage, and BI reporting outputs
- Lead risk-based exploratory testing where automated coverage falls short, and build the frameworks that let the broader team do the same
- Stay hands-on. Write fixtures, review Playwright code, debug flaky suites, and trace escaped defects back to their structural cause
What we're looking for
Technical foundation
- 8+ years in software QA or test engineering, including time spent defining quality strategy and building the infrastructure other engineers rely on, not just executing test plans
- You've built test automation that others inherit: shared frameworks, fixture systems, API contract harnesses, CI quality gates. You made the structural decisions, not just contributed to them
- Deep experience with Playwright, or a close equivalent such as Cypress, at the framework level. You've set the patterns and abstractions a team lives with for years, and you know what that responsibility requires
- Strong TypeScript and JavaScript, and comfort owning the language your automation platform runs on
- Strong SQL. You can validate data transformations, trace lineage, and catch anomalies that take real reasoning to anticipate
- You've shaped how API quality is defined across a product: what gets contract-tested, what gets mocked, where schema validation lives, and where failure handling needs to be explicit
- You get up to speed on an unfamiliar system fast enough to form a real opinion about its risk surface
- Bachelor's in Computer Science, Software Engineering, or equivalent experience
AI-first engineering
This is core to how we work. AI accelerates how we generate, validate, and improve coverage. The judgment, standards, and ownership behind it stay human. At Staff level, you define how the team uses AI tooling, teach others to use it well, and build the infrastructure that sets its ceiling.
- Fluent with AI coding assistants such as Claude Code or Copilot as a primary working tool across test generation, debugging, root-cause analysis, and spec review
- You diagnose weak AI output at its source, whether that's the prompt, the context, or the underlying infrastructure, and fix it there instead of working around it
- You hold AI-generated tests to the same bar as production code: assertion depth, edge case coverage, flake risk, PII safety, and schema coverage
- You build the shared prompt libraries, context templates, and evaluation rubrics the team uses to generate and assess AI output consistently
- You know where deterministic approaches are required, and you keep AI out of critical paths that need them
- You stay current on the tooling and bring what's worth adopting back to the team, backed by evidence rather than hype
Accessibility
DocuPet is committed to WCAG 2.2 Level AA across our digital properties. Quality owns making that real in the build and test process, not just at audit time.
- Experience automating accessibility checks inside a browser automation framework, ideally axe-core with Playwright or a commercial platform such as Level Access
- A clear understanding of what automated accessibility testing does and doesn't catch. Automated scans surface roughly a third of real WCAG issues, so you pair them with manual and exploratory checks
- Ability to stand accessibility up as a CI gate: WCAG 2.2 AA rules running on every pull request, with clear reporting and violations grouped by root cause so the team isn't triaging the same issue across many files
Staff-level qualities
- Organizational scope. You think in systems and teams, not tickets and test suites. You see how a flaky test or an unclear spec ripples into CI trust, release confidence, and team velocity, and you address the cause rather than the symptom.
- Influence through craft. Engineers follow your standards because they hold up, not because of a title. That matters most on a team learning automation for the first time.
- Player-coach. You own the technical direction and still do the work. You review a teammate's first fixture library with the same care you bring to a leadership conversation.
- Judgment under ambiguity. At this scope there isn't always a clear answer. You decide what to test deeply, what to skip, and what to escalate, and you make the tradeoff explicit rather than leaving it implicit.
- Clear and direct. You give actionable feedback early. You raise concerns at the requirements stage, not the post-mortem, and you write clearly enough that a spec becomes a reliable test input.
Nice to have
- Experience taking a manual QA team through the transition into automation
- Depth in data and pipeline testing: ETL, schema evolution, and BI output validation
- Experience with agentic testing workflows or building verification systems around AI-generated code
- Familiarity with the Symfony and PHP stack our product runs on
- Experience as the first or founding automation engineer on a platform
Job Type: Full-time
Pay: $130,000.00-$170,000.00 per year
Benefits:
- Dental care
- Life insurance
- Paid time off
- Vision care
Experience:
- AI coding assistants (Claude Code, Copilot, or similar): 1 year (required)
- SQL: 6 years (required)
- Software quality assurance: 8 years (required)
- Creating use cases and unit tests: 7 years (required)
- Testing RESTful APIs: 5 years (required)
- Creating and maintaining test automation frameworks: 6 years (required)
- Playwright, Cypress or equivalent automation framework: 6 years (required)
- data-intensive web applications testing: 7 years (required)
Language:
Work Location: Remote
