Senior QA Lead - San Francisco

Giga•San Francisco, CA

About The Position

We're looking for a QA lead to own quality across our AI agent platform and run a distributed testing team. You'll define what "ready to ship" looks like, build the test strategies and processes from the ground up, and be the person in SF who keeps quality on track as the team scales. This is a hands-on QA role. You'll write test plans, execute testing, and manage a team of offshore QA testers — not write code or build automation frameworks. You'll work closely with engineering and product to understand what's being built, identify where things can break, and make sure nothing ships until it's been thoroughly validated. If you've run QA for a fast-moving product team and know how to keep quality high without slowing everything down, this is your role.

Requirements

Have 7+ years of experience in QA or quality assurance, with a track record of leading test efforts for complex products
Have managed or led distributed QA teams, ideally across time zones, and know how to keep offshore testers productive, motivated, and aligned with the SF engineering team
Are excellent at test planning — you can look at a feature, think through the edge cases, and build a test strategy that catches the things engineers miss
Have experience testing conversational AI, voice systems, chatbots, or other non-deterministic products where "correct" isn't always a single answer
Are a clear, organized communicator — your bug reports are detailed, your test plans are thorough, and your status updates give leadership confidence
Can push back on engineering when something isn't ready to ship, but also know when good enough is good enough for the stage the company is at
Are comfortable being the person who builds the QA function from scratch — defining the processes, picking the tools, and setting the bar for quality
Are excited about AI and want to figure out what quality assurance looks like for products that didn't exist five years ago

Responsibilities

Test strategy and planning: Defining test plans, acceptance criteria, and coverage matrices for new features across our voice, chat, and email AI agents
AI agent validation: Testing agent conversations for accuracy, tone, hallucinations, and edge cases — figuring out how to systematically QA outputs that are different every time
Release management: Owning the QA gate for releases, coordinating test cycles, managing regression suites, and signing off on production readiness
Team management: Running a distributed QA team across SF and offshore locations — assigning work, reviewing results, setting standards, and keeping the team aligned across time zones
Bug triage and reporting: Classifying, prioritizing, and clearly documenting defects so engineering can reproduce and fix issues efficiently
Process design: Building the QA processes, workflows, and documentation that let us scale testing as the product and team grow