AI Agent Evaluation Engineer

Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, Ping Wu , founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences. Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta. About the role Cresta’s AI Receptionist answers the phone for small businesses that would otherwise miss the call. It handles millions of conversations a month today, making sure no call goes unanswered and helping owners complete the front-office tasks that keep their business running, and it does so across hundreds of customers. Whether those conversations are actually good is the single most important question in the business, and it deserves a full-time owner. That’s you. As our first AI Agent Evaluation Engineer, you define what quality means for every agent we run, you build the systems that measure it across every conversation, and you are the person the company trusts when the question is “how good is it, really?

” The work combines hands-on conversation analysis, rigorous evaluation design, and engineering execution: reading and annotating real conversations to learn how the agent actually fails, building the automated evaluation systems (LLM-based judges, golden sets, regression suites) that measure those failures at scale, and keeping those systems honest by calibrating them against human judgment. Fixing what you find is welcome but not the core of the job; an accurate, prioritized picture of agent quality is. What You'll Do Evaluation Design & Quality Definition - Define and maintain the evaluation rubric for the agent across three axes: adherence (did it follow its design, call the right tools, respect guardrails), outcome (did the conversation achieve its goal), and conversation experience (latency, turn-taking, tone, repetition). - Write the annotation guidelines and failure taxonomy the team uses to talk about failures precisely; run calibration sessions and measure inter-rater agreement. - Classify and prioritize failure modes by severity and frequency so engineering and product work on what matters most. Conversation Analysis & Annotation - Review production conversations by hand every week, leverage QA partners; open-code failures, cluster them, and turn the clusters into the evaluations worth automating. - Trace each failure to its likely cause (prompt, knowledge gap, tool or flow bug, speech recognition) and route it to the right owner with enough evidence to act on. - Evaluate across languages and report quality by segment (language, accent, audio quality, intent), not just in aggregate. Scalable Evaluation Systems - Build and operate the evaluation pipeline that scores every production conversation, combining automated checks, LLM-based judges, and human review. - Calibrate every automated judge against human labels before trusting it; treat judge error as the primary measurement risk. - Design sampling strategies (stratified by intent, language, caller type, audio conditions) so that reported changes in quality are real. - Build and maintain the golden set and regression suite that every prompt, model, knowledge, and flow change is validated against before release.