AI Evaluation Specialist Jobs
AI Evaluation Specialist jobs are open across AI labs, tech companies, and enterprise software firms, from junior annotator-adjacent roles to senior evaluation lead positions, with specializations in model quality assessment, red-teaming, and benchmark design. See the openings below and apply to the ones that match your experience.
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INTRODUCTION
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.
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.
Coverage & Edge Cases:
- Maintain the failure-mode coverage map: which behaviors are tested, partially tested, or unmeasured; close the gaps.
- Design 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.
- Define the quality and latency thresholds the agent is held to.
Reporting & Cross-Functional Collaboration:
- Report quality trends to internal stakeholders and customers clearly, with uncertainty stated and without burying bad news.
- Partner with forward-deployed engineers, backend engineers, product managers, and the voice/language engineering team to turn findings into prioritized recommendations.
- Document methodology so results are reproducible and the evaluation practice outlives any one person.
WHAT WE'RE LOOKING FOR
- 3+ 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.
- Bachelor’s or Master’s degree in Computer Science, Computational Linguistics, Cognitive Science, Statistics, or a related field, or equivalent practical experience.
- Built and operated an end-to-end evaluation system for a production LLM product (agent or otherwise): rubric, human labels, automated judge, regression suite.
- Deep 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.
- Fluent 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.
- Strong 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.
- Able to write a rubric that independent reviewers would score the same way, and to measure whether they do.
- Able to design a test from an open-ended quality question: you decide what to measure and how, rather than executing a predefined plan.
- Willing 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.
- Professional fluency in at least one language other than English, sufficient to evaluate multilingual agent behavior directly.
- Strong written communication; you author the guidelines others follow.
NOTE ON OUR HIRING PROCESS
We 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.
PERKS & BENEFITS
We offer a comprehensive and people-first benefits package to support you at work and in life:
- Comprehensive medical, dental, and vision coverage with plans to fit you and your family
- Flexible PTO to take the time you need, when you need it
- Paid parental leave for all new parents welcoming a new child
- Retirement savings plan to help you plan for the future
- Remote work setup budget to help you create a productive home office
- Monthly wellness and communication stipend to keep you connected and balanced
- In-office meal program and commuter benefits provided for onsite employees
COMPENSATION AT CRESTA
Cresta’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. The 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.
Our 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 recruiting@cresta.ai (if you are opting out, please use the subject ‘Opt-Out’ in your email).
Recording & 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.
Recruiting 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. Any outreach claiming to be from Cresta via other sources should be ignored.
INTRODUCTION
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.
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.
Coverage & Edge Cases:
- Maintain the failure-mode coverage map: which behaviors are tested, partially tested, or unmeasured; close the gaps.
- Design 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.
- Define the quality and latency thresholds the agent is held to.
Reporting & Cross-Functional Collaboration:
- Report quality trends to internal stakeholders and customers clearly, with uncertainty stated and without burying bad news.
- Partner with forward-deployed engineers, backend engineers, product managers, and the voice/language engineering team to turn findings into prioritized recommendations.
- Document methodology so results are reproducible and the evaluation practice outlives any one person.
WHAT WE'RE LOOKING FOR
- 3+ 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.
- Bachelor’s or Master’s degree in Computer Science, Computational Linguistics, Cognitive Science, Statistics, or a related field, or equivalent practical experience.
- Built and operated an end-to-end evaluation system for a production LLM product (agent or otherwise): rubric, human labels, automated judge, regression suite.
- Deep 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.
- Fluent 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.
- Strong 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.
- Able to write a rubric that independent reviewers would score the same way, and to measure whether they do.
- Able to design a test from an open-ended quality question: you decide what to measure and how, rather than executing a predefined plan.
- Willing 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.
- Professional fluency in at least one language other than English, sufficient to evaluate multilingual agent behavior directly.
- Strong written communication; you author the guidelines others follow.
NOTE ON OUR HIRING PROCESS
We 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.
PERKS & BENEFITS
We offer a comprehensive and people-first benefits package to support you at work and in life:
- Comprehensive medical, dental, and vision coverage with plans to fit you and your family
- Flexible PTO to take the time you need, when you need it
- Paid parental leave for all new parents welcoming a new child
- Retirement savings plan to help you plan for the future
- Remote work setup budget to help you create a productive home office
- Monthly wellness and communication stipend to keep you connected and balanced
- In-office meal program and commuter benefits provided for onsite employees
COMPENSATION AT CRESTA
Cresta’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. The 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.
Our 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 recruiting@cresta.ai (if you are opting out, please use the subject ‘Opt-Out’ in your email).
Recording & 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.
Recruiting 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. Any outreach claiming to be from Cresta via other sources should be ignored.
AI Evaluation Specialist Jobs by Experience Level
See All 80 AI Evaluation Specialist Jobs
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Find AI Evaluation Specialist JobsAI Evaluation Specialist Job Market
Who's Hiring
- Apple14
- Capital One6
- Meta4
- Alpheva AI4A
- Volga Partners4
Top Industries Hiring
- Technology & Software
- Education
- Telecommunications
- Electronics & Hardware
What Employers Look For
The qualifications that appear most often in AI evaluation specialist jobs.
- Experience with human preference labeling, RLHF pipelines, or model output scoring
- Strong written communication skills for drafting evaluation rubrics and critique justifications
- Familiarity with large language model behavior, failure modes, and prompt sensitivity
- Ability to maintain high inter-rater reliability and calibration across scoring tasks
- Background in linguistics, cognitive science, computer science, or a related field
- Experience with safety evaluation, red-teaming, or adversarial prompt testing preferred
Tips for Your AI Evaluation Specialist Job Search
Tailor your resume to evaluation frameworks
Highlight specific evaluation methodologies you've applied, such as human preference labeling, adversarial testing, or rubric-based scoring. Generic QA experience won't land interviews here. Name the model types and task domains you've assessed, whether that's text generation, code output, or multimodal responses.
Apply early to roles that fit
Migrate Mate lists ai evaluation specialist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings by evaluation task type
Job titles vary widely: some postings say 'AI Quality Analyst,' others say 'RLHF Specialist' or 'Model Evaluator.' Search by task keywords like preference ranking, instruction following, or safety evaluation to surface relevant openings that generic title searches miss.
Build a portfolio of annotated critique samples
Prepare two or three documented examples where you identified model failure modes, wrote evaluation rubrics, or scored outputs against defined criteria. Concrete artifacts matter more than credentials when hiring managers are comparing candidates with similar backgrounds.
Prepare for live scoring or annotation exercises
Many ai evaluation specialist interviews include a take-home or live task where you rate model outputs against a rubric. Practice writing clear, consistent justifications for your scores. Interviewers look for calibration and reasoning, not just the score you chose.
Negotiate scope alongside compensation
When you receive an offer, clarify which model families and task types you'll evaluate, whether the role includes rubric design or only scoring, and how performance is measured. Scope creep in this role is common, and what you agree to upfront shapes your day-to-day significantly.
AI Evaluation Specialist Jobs: Frequently Asked Questions
Which companies are hiring the most ai evaluation specialists?
Apple, Capital One, and Meta are hiring the most ai evaluation specialists right now, with openings concentrated in California, Virginia, and Washington, based on current listings on Migrate Mate as of November 2026. Demand is especially high at AI labs and enterprise software companies expanding their model quality and safety teams.
How many ai evaluation specialist jobs are remote?
About 83% of ai evaluation specialist openings are fully remote or hybrid as of November 2026, reflecting how much of this work involves asynchronous output review rather than on-site collaboration. Roles focused on annotation, preference labeling, and rubric-based scoring tend to be the most remote-friendly, while positions tied to model deployment teams more often require hybrid availability.
How do you become an ai evaluation specialist?
Start by developing hands-on experience with language model outputs, either through freelance annotation work, open-source evaluation projects, or structured courses on AI safety and alignment. Build familiarity with evaluation frameworks like preference ranking and rubric design. Then document your work with concrete examples of rubrics you wrote or failure modes you identified, since most hiring processes include a practical scoring exercise.
How do you get hired as an ai evaluation specialist with little experience?
Focus on demonstrating calibration and clear reasoning rather than credentials. Contribute to open evaluation datasets or public red-teaming initiatives to build a record of scored outputs. Roles labeled 'AI Quality Analyst' or 'Content Reviewer' at AI-focused companies often serve as entry points, and hiring managers in this space weigh writing quality and analytical consistency heavily over formal degrees or prior job titles.
What does the ai evaluation specialist interview process look like?
Most processes include a recruiter screen, a hiring manager conversation focused on your experience with model output assessment, and a practical task where you score or critique a set of model responses against a rubric. Some companies add a calibration round where your scores are compared against a reference rater. Final stages often involve a team conversation about evaluation methodology and how you handle ambiguous or borderline outputs.
Where can I find and apply to ai evaluation specialist jobs?
You can find and apply to ai evaluation specialist jobs on Migrate Mate, which lists current openings from across the United States. Search for roles that match your background in model evaluation, annotation, or AI quality assessment, then apply directly to each listing that fits your experience and preferred location or remote arrangement.
See All 80 AI Evaluation Specialist Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Evaluation Specialist Jobs