Quality Engineering Lead

About Norm Ai

Norm Ai is the Compliance AI Platform for legal standards-based reasoning and workflow automation.

We developed the first Domain Specific Language (DSL) that fully represents regulatory requirements in AI code. Deployed through our enterprise platform, this DSL empowers Norm clients to transform workflows and enforce compliance checks at the core of their business operations.

By building and deploying Government-Grade Regulatory AI, we are setting the standard for compliance processes within the world's largest institutions and paving the way for broader deployment of AI agents in highly regulated workflows. Our rapidly expanding client base manages a combined $17 Trillion in AUM.

Our team comprises software engineers from Palantir, Google, Meta, AWS, Harvard, Stanford, and MIT, and legal engineers from Harvard Law, Stanford Law, Yale Law, Sullivan & Cromwell, Simpson Thacher, Davis Polk, Greenberg Traurig, the SEC, and FINRA.

We have secured investments from Vanguard, Blackstone, Bain Capital, Coatue, Craft Ventures, New York Life, Citi, TIAA, Larry Summers, and Marc Benioff.

About the role

We’re looking for our first Quality Engineering Lead to establish and scale our QA strategy at the forefront of AI-powered enterprise compliance software. As our first dedicated quality hire, you’ll define and implement test processes, while partnering closely with engineering, product, and design to ensure our most complex and high-impact workflows are reliable and regression-resistant.

This is a hybrid role: part technical contributor, part process leader, and part future team building. You’ll own the foundation of our quality efforts and help us evolve toward a scalable model that supports fast iteration without sacrificing stability.

Core Responsibilities:

• Define and operationalize a pragmatic quality strategy that supports fast-moving, engineering-owned development.

• Lead by example–writing, organizing, and maintaining regression test plans (manual and automated) for critical flows.

• Partner with engineers to identify test gaps, reduce flakiness, and improve coverage across branching workflows.

• Introduce and enforce lightweight release-readiness practices for high-risk changes.

• Build tooling or partner with platform engineers to improve test infrastructure, data generation, and CI reliability.

• Track and share key quality metrics (e.g. regression trends, test pass rates, flaky test triage).

• Own root cause analysis for regressions and help ensure long-term prevention.

• Develop the roadmap to scale the QA function, including evaluating when and how to grow a team.

Skills & Experience - Core:

• 5+ years of experience in software engineering, QA, quality engineering, or test infrastructure roles

• Strong experience designing and executing test strategies for complex, evolving enterprise products

• Hands-on experience with end-to-end testing tools (e.g. Playwright, Cypress), and writing maintainable automated tests

• Comfort acting as both an individual contributor and quality process owner

• Experience with CI/CD systems and improving test visibility and observability

• High ownership mindset with strong collaboration and communication skills

• Ability to work cross-functionally with engineers, PMs, and designers to prioritize quality in the product lifecycle

Skills & Experience - Pluses:

• Experience as a first QA hire or building QA functions from scratch

• Exposure to AI-powered, probabilistic, or highly dynamic UIs

• Familiarity with testing in regulated or enterprise environments

• Experience mentoring or managing other QA engineers, contractors, or automation specialists

What success looks like after 30 days

• You’ve mapped out key risk areas in the product and identified priority test coverage gaps

• You’ve proposed a practical QA model that supports team-owned quality and minimizes regressions

• You’ve partnered with engineering leads to improve existing test coverage and reliability in CI

• You’ve begun organizing and documenting regression testing practices

What success looks like after 90 days

• You’ve led structured regression testing cycles for high-risk workflows, with measurable impact

• You’ve introduced key process improvements that help engineers ship with more confidence

• You’ve established visibility into test failures, cov