Join ECFX, a company transforming how the legal industry works through technology. Every day our platform pulls court notices and legal documents from hundreds of court portals and e-filing systems, categorizes them with AI, and delivers them into our customers' document and case management systems. When something is late, dropped, or misfiled, a law firm can miss a deadline.
We're looking for a hands-on QA Engineer who loves finding what others miss, and who uses AI to do it faster and deeper. Our engineers build with AI coding agents, so changes arrive quickly and in volume. You'll be the person who works out what could break and tests it thoroughly by hand, using AI as your co-pilot for test design, test data, investigation, and reporting. This is a testing role first. If you also enjoy building automation, there's room to use AI to create targeted automated checks where they pay off. You will have a real say in how we build, test, and ship software.
What You'll Engage In
- Test Smarter with AI: Use AI tools (such as Claude, Claude Code, Cursor, or ChatGPT) to read tickets, merge requests, and code diffs; map out risks and edge cases; draft test charters and checklists; and decide where to focus. You bring the judgment about what matters.
- Explore and Uncover: Run risk-based exploratory, functional, and regression testing across our Vue dashboard, admin tools, email notifications, and integrations. Find defects before customers do.
- Validate What Matters Most: Make sure documents are retrieved, parsed, categorized, stored, and delivered correctly across many external sources: court portals, 2FA/OTP logins, and integrations such as Clio, Filevine, iManage, and NetDocuments. Hunt for silent failures, where a document is dropped but the run reports success.
- Test Below the UI: Much of our work has no screen to click. Verify backend and platform changes by hand, using API calls, SQL queries, logs, and dashboards (Grafana/Loki, Sentry), with AI helping you write the queries and make sense of the output.
- Build Realistic Test Scenarios: Use AI to generate test data, sample court notice emails, and edge-case documents, and drive our simulators and test environments to reproduce hard-to-reach situations.
- Test Our AI Features: Evaluate our AI document categorization and our LLM-facing tools (an MCP server used by AI assistants). Build sample sets, check accuracy and consistency, try to break them with unusual or hostile inputs, and flag when a model or prompt change makes results worse.
- Guard Security and Access: Check logins (password, SAML SSO, OAuth), user roles and permissions, and that one firm can never see another firm's data. Verify security fixes without breaking what already works.
- Reproduce and Triage: Investigate customer-reported and production issues, reproduce them reliably, and write bug reports clear enough that an engineer, or an AI agent, can act on them right away.
- Automate Where It Pays Off (Optional): If automation is in your toolkit, use AI to create focused automated checks, such as Playwright scripts or API checks, for high-risk flows you find yourself retesting.
- Agile Collaboration: Work with engineers and product managers in sprint planning, backlog refinement, and retrospectives. Help write testable acceptance criteria and raise quality risks early
What You're Likely to Bring
Professional Experience: 3 to 7 years in software quality assurance for web or SaaS applications, with deep experience in exploratory and manual testing of complex, data-heavy systems.
AI Fluency
- ·You use AI assistants in your testing every day and can give concrete examples: generating test ideas and edge cases, building test data, writing SQL or log queries, summarizing logs or diffs, reproducing bugs, and drafting clear reports.
- Skill at prompting and giving context, such as feeding in the ticket, the diff, and the acceptance criteria to get useful output instead of generic answers.
- Healthy skepticism. You verify what AI tells you, recognize when it is confidently wrong, and never let it replace your own judgment about risk.
- An understanding of how to test systems that don't always give the same answer, such as AI features, by using repeated trials, sample sets, and "is this answer right?" checks rather than exact matches.
- Careful handling of data. You use synthetic or masked data with AI tools and never paste customer or court data into unapproved services.
Technical Proficiency
· Comfort with the command line, Git, and reading logs. Some experience with Docker or Kubernetes (such as port-forwarding or checking pod logs) is a plus.
· Experience with defect tracking and test management in Jira.
How You Work
- Analytical Mindset: Sharp attention to detail, curiosity, and a methodical approach to breaking software and finding root causes.
- Communication Excellence: Strong written and verbal communication for bug reports, test notes, and status updates. Your reports are precise enough that another person, or an AI agent, can pick them up and act.
- Ownership: When something seems "untestable," you find a way, whether that's a query, a log search, a simulator, or a question to the right engineer, rather than skipping it.
Requirements
- Experience writing or maintaining automated tests (for example Playwright, Selenium, or API test scripts), including building them with AI and reviewing and fixing what it produces.
- Experience testing AI or LLM features, such as checking classification accuracy, spotting hallucinations, or testing for prompt injection.
- SQL skills for checking data in PostgreSQL.
- Experience testing APIs directly with tools like Postman, curl, or Bruno, and an understanding of HTTP, REST, and JSON.
- Experience with web security testing, including authentication and sessions, roles and permissions, OAuth2/SAML, CSRF, and XSS.
- Experience using AI browser tools (for example Playwright MCP or browser agents) to speed up exploratory testing.
- Experience testing integrations with third-party systems, email-driven workflows, or browser automation against external sites.
- Experience with accessibility testing (WCAG 2.2 AA).
- Knowledge of the legal domain, especially court e-filing, docketing, or legal document management.
- ISTQB or a similar QA certification.
- An understanding of or experience with the Lean Startup approach.
Why QA Engineers Like It Here
- Modern Tooling: Company-provided AI tools, with clear guidelines for using them safely.
- Real Impact: You'll shape how we test in an AI-assisted team, not follow a fixed script.
- AI in Production: Hands-on work testing real AI features used by law firms every day.
Benefits
- Remote Flexibility: A fully remote role open to every US time zone, Hawaii included.
- Work-Life Harmony: Flexible working hours and a supportive approach to getting work done.
- Comprehensive Coverage: A range of insurance options covering health, dental, vision, and life.
- Discretionary Time Off: We know a healthy work-life balance matters. Our discretionary time off (DTO) policy means that, on top of scheduled vacations and holidays, employees can take unplanned time off when needed, with supervisor approval. Life happens, and we trust our team to manage their time well.
- Employee Stock Options: The opportunity to participate in the company stock option plan.
