Senior Software Quality Engineer

Zipliens is a leading lien resolution company that specializes in streamlining the lien process for personal injury law firms. Our work helps law firms operate more efficiently and helps the injured people they serve access settlement funds more quickly and accurately. We are looking for a senior quality engineer who is passionate about setting the quality standards for a growing engineering team and building the testing and evaluation that keeps complex, data-heavy products working correctly at scale.

Zipliens is looking for a Senior Quality Engineer to join our team and lead quality for the applications we build for the legal industry. After several years of high growth, Zipliens has become a leader in lien resolution, a process required in most personal injury settlements. The software we build supports that work, so the quality of what we ship has a clear, real-world impact on law firms and the people they represent.

We're scaling engineering significantly this year, and quality engineering is growing along with it. QA at Zipliens is embedded in the engineering teams and combines exploratory testing with automation. As the senior on the team, you'd set the standards the rest of the team works to: what sufficient coverage looks like, what gates a merge and a deploy, which frameworks and tools we standardize on, and how the quality engineers we add this year learn to think about risk.

You'll have context available rather than a blank slate, and your judgment will shape the team's standards rather than just inheriting them.

We also build with AI as a first-class part of the workflow, and that applies to testing as much as to development. Our product uses LLMs to extract structure from medical records and lien correspondence, so deciding how non-deterministic output gets validated before release is a core part of this role.

We also use agentic coding tools and internal skills and harnesses we've written ourselves to build and maintain automation, and you'd be expected to use them well, set the standard for how their output gets reviewed, and evaluate new tools as they come along.

Requirements

Responsibilities:

• Lead quality for medium to large features, including ones that cross team boundaries. You'll own the risk analysis, the test strategy, the release criteria, and the final sign-off.
• Set, follow, and advocate for the team's test standards and frameworks, including what sufficient coverage means for the team and what a passing build is expected to guarantee.
• Own the automation and CI/CD gating strategy: which suites run at which stage, what gates a merge versus a deploy, and how we keep suite runtime and flakiness under control so engineers trust the results.
• Own the evaluation methodology for our LLM features, including sampling and significance, rubric design, regression gates on prompt and model changes, production quality monitoring and drift alerts, and re-running golden sets when a vendor updates a model.
• Set the team's standards for validating AI-generated output, both in the product and in the tests and tooling we generate with AI.
• Collaborate with the Product Owner to define Acceptance Criteria and release criteria, and serve as the primary QA point of contact for cross-functional planning.
• Evaluate and introduce new tools and AI capabilities for the team.
• Mentor the quality engineers on the team and coach engineers on exploratory technique and risk-based testing. We're adding people quickly this year; the seniors we hire now establish what good looks like for everyone who follows.
• Weigh in on the testability of application designs early, while changes are still cheap to make.

Qualifications:

• 5+ years on a QA or software development team, with experience across a range of products and test approaches.
• Expert-level skills in at least one of exploratory testing and test design, test automation and infrastructure, or AI/LLM evaluation, and solid proficiency in the other two. At this level we expect you to have set the methodology for a team, not only followed one.
• Experience setting test standards and choosing frameworks for a team, and getting the team to adopt them.
• Significant experience building and owning automation in a modern framework (Playwright, Cypress, pytest, or similar), including designing the CI/CD gating strategy around it.
• Comfortable in code. Our backend is Python/Django and our frontend is React, and you'll be reviewing test code, reading changes for risk, and contributing to shared test infrastructure.
• Strong SQL and PostgreSQL. Verifying a workflow here usually means verifying the data it produced as well as what the UI shows.
• Hands-on experience evaluating LLM or ML features in production: golden datasets, acceptance thresholds, calibrating LLM judges against human labels, and monitoring quality after release.
• Experience using agentic coding tools for test work, and a clear view of where their output needs human review.
• Experience mentoring quality engineers or developers and raising standards on a growing team.
• Excellent written and verbal communication - you'll be defining release criteria with product, explaining risk to non-technical stakeholders, and making the case for quality investment to engineering leadership.
• Nice to Have: Experience testing document processing or extraction from unstructured data, including defining acceptance thresholds for it.
• Experience building or owning test infrastructure shared across multiple teams.
• Experience testing third-party integrations, especially where the counterparty's documentation isn't solid and you need contract tests, sandboxes, or recorded fixtures to work around it.
• Familiarity with AWS and Kubernetes. We have an SRE who owns the cluster - you won't be running it, but you'll be working with them on test environments and tracking down where a failure happened.
• Experience handling sensitive data such as PHI, or working under HIPAA-style requirements.