Senior Quality Engineer

Job Description

Tech Stack : Playwright, React, FastAPI (Python), PostgreSQL, AI coding and testing agents About the role Are you a Senior Quality Engineer who still loves writing and debugging tests every day? Our client is a fast-growing, AI-native research platform. Strategy consultants and private equity teams use it to run surveys from project scope through to final insight, often during commercial due diligence. The analysis it produces ends up in decisions that matter, so accuracy and trustworthy AI output are critical. This is a hands-on specialist role within the engineering team. You'll do the testing, investigation and automation yourself, while pod leads own the wider quality strategy. AI sits on both sides of the job: you'll test an AI-powered product and help shape how engineers and agents build software. Experience and qualifications: Experience in a hands-on quality engineering, test automation or SDET role, with most of your recent time spent testing rather than leading Strong experience writing maintainable Playwright tests (TypeScript or Python) Daily use of AI tools in your testing, and a clear way of checking their output Confident API debugging and SQL skills, ideally with PostgreSQL A proven ability to find the root cause of intermittent test, CI, test data and environment failures, rather than relying on retries Sound release judgement: you can find the edge cases that matter and explain what should block a release A project with real automated tests that you can walk through in detail, whether professional or personal A genuine interest in combining manual verification with building better automated coverage Experience in SaaS product companies, data or analytics platforms, or AI products (advantageous) Responsibilities: Maintaining and extending the Playwright integration and end-to-end suite, including visual checks, and proving that key tests catch the faults they target Using coding and testing agents every day, critically reviewing their output, and improving the shared instructions and skills the whole engineering team uses Running focused manual regression and keeping repeatable release plans that cover affected journeys, edge cases and known gaps Exploring complex workflows, lifecycle states, permissions and data, and agreeing expected outcomes early with Product and Design Separating release blockers from minor issues and clearly explaining impact, evidence and uncertainty to the release lead Independently investigating CI, fixture, authentication and environment failures, and escalating with evidence Turning defects and repeated manual checks into maintainable automated coverage Pairing with engineers on improvements, supporting cross-pod releases and working with the AI team on verification #J-18808-Ljbffr