QA Lead

The role. Automation-first quality engineering for a platform combining voice, desktop, intelligence, and AI. We emphasize AUTOMATION — we do not do manual QA here. Weekly code deploys are the heartbeat: when a QA branch is ready, regressions run fast, results report fast, and the train doesn't wait. Startup environment: 1-week sprints, fail fast, move forward.

What you'll own. Automation coverage for your squads' services · fast-turnaround regression suites gating weekly deploys · rapid detection and reporting — quality signal in minutes, not days · load/stress coverage for your services' latency budgets · your committed timelines.

Who you are. Self-starter with grit and a show-me mentality. You consider yourself exceptional, love new technology, adapt fast when the stack changes under you, and use AI tools daily to multiply velocity — including AI-assisted test generation. A team player who likes winning, and a partner to dev, not a gate: when something breaks, you help the dev team isolate it fast.

Requirements

• 8+ years SDET; writes real automation code (Go / Python), not manual scripts. Automation-first is a conviction, not a preference.
• Master issue-isolator and root-cause debugger — you narrow a failure to the service, the commit, the event; dev teams love your bug reports because they're half the debugging done.
• Fast regression discipline — parallelized suites, smart test selection, results in minutes; quality at weekly-deploy speed without becoming the bottleneck.
• Comfortable in high-load testing environments — load, stress, and soak testing of high-throughput, low-latency systems; you know how to find the knee of the curve.
• Experience testing event-driven systems, WebSocket flows, and browser extensions — ordering, race conditions, real-time state.
• CI-native mindset — tests wired into trunk-based CI/CD; flake management as a first-class discipline (a flaky suite is a broken suite).
• Contract/API testing between services; synthetic data and traffic generation (simulated calls, event streams, audio) for repeatable real-time testing.
• Testing AI outputs — eval-style assertions for non-deterministic LLM/ML features — a strong plus.