{"id":4213969,"url":"https://alion.io/job/cresta-ai-agent-evaluation-engineer","title":"AI Agent Evaluation Engineer","company":{"id":34,"name":"Cresta","domain":"cresta.com","url":"https://alion.io/company/cresta-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":79,"open_postings":87,"ghost_share":0.264,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":134,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Canada","United States"],"countries":["CA","US"],"hiring_countries":["US","CA"],"hiring_countries_total":2,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-12T21:51:49Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-13T02:05:33Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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. \nBorn 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.\nOver 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.\nAbout the role:\nCresta’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.\nAs 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?”\nThe 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.\nWhat You'll Do:\nEvaluation Design & Quality Definition\nDefine 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).\nWrite 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.\n\nConversation Analysis & Annotation\nReview production conversations by hand every week, leverage QA partners; open-code failures, cluster them, and turn the clusters into the evaluations worth automating.\nTrace 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.\nEvaluate across languages and report quality by segment (language, accent, audio quality, intent), not just in aggregate.\nScalable Evaluation Systems\nBuild and operate the evaluation pipeline that scores every production conversation, combining automated checks, LLM-based judges, and human review.\nCalibrate every automated judge against human labels before trusting it; treat judge error as the primary measurement risk.\nDesign sampling strategies (stratified by intent, language, caller type, audio conditions) so that reported changes in quality are real.\nBuild and maintain the golden set and regression suite that every prompt, model, knowledge, and flow change is validated against before release.\nCoverage & Edge Cases\nMaintain the failure-mode coverage map: which behaviors are tested, partially tested, or unmeasured; close the gaps.\nDesign evaluations for behaviors no standard harness covers: high-stakes conversation types such as handoffs to a human, misuse and unwanted-caller handling, and resilience under upstream failures.\nDefine the quality and latency thresholds the agent is held to.\nReporting & Cross-Functional Collaboration\nReport quality trends to internal stakeholders and customers clearly, with uncertainty stated and without burying bad news.\nPartner with forward-deployed engineers, backend engineers, product managers, and the voice/language engineering team to turn findings into prioritized recommendations.\nDocument methodology so results are reproducible and the evaluation practice outlives any one person.\nWhat We're Looking For:\n3+ years of professional experience in software engineering, applied ML, or data-intensive research roles, including at least 1-2 years working hands-on with LLM-based systems in production.\nBachelor’s or Master’s degree in Computer Science, Computational Linguistics, Cognitive Science, Statistics, or a related field, or equivalent practical experience.\nBuilt and operated an end-to-end evaluation system for a production LLM product (agent or otherwise): rubric, human labels, automated judge, regression suite.\nDeep understanding of evaluation methodology: what golden sets, judges, regression suites, and calibration are for, and when each fails. Tool-agnostic; you will work in our in-house tooling.\nFluent in how LLM agents work: prompts, tool calls, context handling, multi-turn state. You can read a trace and tell what the agent was doing and why.\nStrong programming skills in Python and experience with LLM APIs; able to build and ship evaluation tooling independently. Fine-tuning and ML frameworks are not required.\nAble to write a rubric that independent reviewers would score the same way, and to measure whether they do.\nAble to design a test from an open-ended quality question: you decide what to measure and how, rather than executing a predefined plan.\nWilling to do non-scalable work weekly, both yourself and by directing our QA partners: listening to calls, labeling conversations, building a failure taxonomy from scratch.\nProfessional fluency in at least one language other than English, sufficient to evaluate multilingual agent behavior directly.\nStrong written communication; you author the guidelines others follow.\nNote on Our Hiring Process\nWe highly value your time and engineering craftsmanship. To ensure a streamlined and objective process, the first step for qualified applicants following an initial resume review is a self-paced CoderPad technical assessment. This allows you to showcase your practical coding and problem-solving skills in a real-world environment before moving into conversational interview rounds.\nPerks & Benefits:\nWe offer a comprehensive and people-first benefits package to support you at work and in life:\nComprehensive medical, dental, and vision coverage with plans to fit you and your family\nFlexible PTO to take the time you need, when you need it\nPaid parental leave for all new parents welcoming a new child\nRetirement savings plan to help you plan for the future\nRemote work setup budget to help you create a productive home office\nMonthly wellness and communication stipend to keep you connected and balanced\nIn-office meal program and commuter benefits provided for onsite employees\nCompensation at Cresta: \nCresta’s approach to compensation is simple: recognize impact, reward excellence, and invest in our people. We offer competitive, location-based pay that reflects the market and what each individual brings to the table.\nThe posted base salary range represents what we expect to pay for this role in a given location. Final offers are shaped by factors like experience, skills, education, and geography. In addition to base pay, total compensation includes equity and a comprehensive benefits package for you and your family.\nOur use of AI in recruiting: We may use AI tools in our recruiting process, which require human review and oversight. If you have questions about our use of AI or wish to opt out, please contact  (if you are opting out, please use the subject ‘Opt-Out’ in your email).\n\nRecording & AI transcription: We do not authorize candidates to record or use AI transcription of any interview by phone, notetaker app, AI glasses, or other device. Candidates may request an exception or accommodation for review during the interview process as needed.\n\nRecruiting impersonation scams: We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. 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