Senior QA Engineer

Vi Operate puts AI agents into the daily operations of large health systems, providers, and pharma organizations. Augment is the voice capability underneath it: the agents that call patients and providers, hold a real conversation, and take action in the systems where the work actually lives. This is a dedicated quality role for that work. Testing a voice agent is not like testing software that returns the same answer twice. A similar call can go three different ways. A model update can quietly change behavior that nothing in a unit test would catch. When a call goes badly, the cause could be the speech recognition, the model, the voice synthesis, the telephony layer, or the integration underneath, and the transcript alone won't tell you which. Making that testable, repeatable, and catchable before a client hears it is the job. You will own quality for agents that call real patients, providers, and caregivers. That means building the test and evaluation systems our engineers develop against, deciding what has to pass before something ships, and being the person who can take a bad call recording and say what actually went wrong. We need someone who has done classical QA properly and has since tested voice and LLM systems in production, because this role needs all three. What You'll Own Voice testing and debugging. Call recordings, transcripts, latency traces, and logs. Isolating which layer failed: speech to text, the model, text to speech, telephony, or the integration behind it. Turning a vague "the call went badly" into a reproducible ticket an engineer can act on. Evaluation and regression testing. Golden test sets, simulated calls, judged transcripts, accuracy on classification and extraction, and structured output validation. Regression coverage that still holds when the underlying model changes. Testing things that aren't deterministic. Deciding what "passing" means when the output varies run to run. Setting tolerances, handling flake honestly rather than by rerunning until green, and keeping the suite trustworthy enough that engineers act on a failure. Release criteria. What has to be true before an agent goes live for a client, and what has to stay true afterward. You set the bar and you hold it, including when a deployment date is pushing on it. Classical QA for the rest of the product. Test plans, regression suites, bug triage, reproductions, and CI integration for everything around the agent. Quality in production. Sampling live calls after release, spotting drift and degradation, and getting it in front of the right engineer before the client raises it. Breaking it on purpose. Red-teaming the agent before someone outside does. Prompt injection through the documents it reads, callers who talk it off script or push it toward giving medical advice, people claiming to be someone they aren't to get patient information out of it. You test that the guardrails and access controls actually hold under pressure, not just that they exist.