ATS-Friendly Resume Template for an AI Evaluation Specialist

Make the resume easy to read and parse

• Use one column and ordinary text. Put your name and contact details in the main document body, followed by clear headings such as Professional Profile, Skills, Experience and Education. Greenhouse identifies columns, tables, images, text boxes, headers and footers as formats that can interfere with parsing; a simple layout is a practical default, not a promise about every hiring system. Greenhouse resume-parsing guidance.
• Write complete job titles, employer names and dates consistently. Use readable words in place of decorative icons or letter spacing. Do not hide keywords in white text or add terms you cannot substantiate.
• Follow the specific application page's file instructions. Employers and platforms accept different formats; Greenhouse's upload list and LinkedIn's application help illustrate why one format rule should not be assumed everywhere. After an upload, inspect and correct any fields the application prefilled; parsers can make mistakes. Greenhouse candidate guidance.
• Make the submitted file searchable and selectable as text. Read the exported copy from top to bottom to catch lost lines, broken characters or reordered sections. Keep a plain-text master so you can adapt it without rebuilding a complex design.
• Use a professional link only if it opens to work you are allowed to share. Do not place confidential prompts, customer records, holdout sets, vulnerability details or internal dashboards in a portfolio.

Choose evidence before keywords

Read the target posting and identify its actual evaluation object: a base model, a retrieval-augmented application, a tool-using agent, a human-review program, a safety control, or an autonomous system. Match your resume to duties you have performed in that setting. Vehicle and robotics evaluation methods are a separate technical specialty; they should not be presented as generic language-model experience.

The bank below is a menu of terms seen across the sampled employer pages, not a list to paste wholesale. Choose a term only when a work bullet, project or qualification can support it. Preserve the employer's wording when it truthfully describes your work, including spelled-out and shortened forms where useful.

• Evaluation design: intended use, evaluation scope, test population, success criteria, benchmark, scenario suite, rubric, holdout, sampling, human evaluation, calibration.
• Model and application behavior: answer correctness, grounding, faithfulness, retrieval relevance, hallucination, robustness, task completion, agent tool use, multi-step workflow, voice or multimodal quality when applicable.
• Measurement and analysis: baseline, scorecard, error analysis, confidence or uncertainty, inter-rater agreement, regression test, before-and-after comparison, quality threshold, trace review.
• Lifecycle and communication: defect reproduction, mitigation retest, monitoring, release-readiness evidence, audit trail, reviewer rationale, risk summary, stakeholder handoff, escalation.
• Tools and methods: list only tools you used. Depending on the role, postings may mention Python, SQL, notebooks, Git, ML frameworks, evaluation harnesses, tracing, dashboards, cloud services or data platforms. Name the exact tool in your experience bullet when it matters to the result.
• Authorized adversarial work: include red teaming, prompt injection, misuse tests or threat modeling only when you performed them within an approved scope and can describe the work without exposing restricted details. These duties appear in a specialist subset of the U.S. vacancy study.

Build evidence-based experience bullets

A strong bullet connects an action to an evaluation object and a traceable result. Use this pattern:

Action + system or test scope + method or artifact + observed finding or operational use.

For each bullet, ask what you can show if an interviewer probes it. The evidence may be a rubric you authored, a versioned case set, a documented comparison, a dashboard, a defect record, a calibration note, or an approved report. A number is useful only when you know its denominator, time window and source. If no defensible metric exists, state the concrete output and who used it instead of inventing one.

Keep decisions and authority precise. “Recommended a hold after a regression review” differs from “approved release.” “Escalated a safety finding” differs from “owned the policy.” Describe your contribution and the handoff. Where the work involved sensitive data or adversarial testing, mention approved scope or controlled environment if it clarifies the role, without disclosing protected material.

Reusable blank resume

The lines below are intentional fill-in fields. Replace them with your own verified information and remove every blank line you did not use before sending the resume.

Contact

Full name: ______________________________
City and state: ______________________________
Email: ______________________________
Phone: ______________________________
Relevant professional or portfolio link, if shareable: ______________________________

Professional Profile

Write two or three sentences that name your actual role or transferable specialty, the AI system or evaluation work you have done, two or three relevant methods, and the kind of evidence you produced.

Core Skills

Evaluation scope, cases and rubrics: ____________________________________________
Measurement and failure analysis: _____________________________________________
AI system types and quality dimensions: ________________________________________
Tools used in relevant work: __________________________________________________
Collaboration, reporting and handoffs: __________________________________________

Professional Experience

Job title: ______________________________
Employer: ______________________________
Location or remote eligibility: ______________________________
Dates, month and year: ______________________________

• Evaluated or built ______________________________ for ______________________________ using ______________________________; produced ______________________________.
• Investigated ______________________________ by ______________________________; documented ______________________________ and handed the finding to ______________________________.
• Improved or retested ______________________________; the observed result or decision use was ______________________________.

Job title: ______________________________
Employer: ______________________________
Location or remote eligibility: ______________________________
Dates, month and year: ______________________________

• Built, reviewed or maintained ______________________________; the evidence was ______________________________.
• Worked with ______________________________ to resolve or escalate ______________________________; documented ______________________________.

Selected Evaluation Work

Use this section only for work that adds evidence beyond your job bullets, such as a shareable project, publication or approved portfolio artifact.

Project or artifact: ______________________________
Your role and dates: ______________________________
Evaluation object, method and observable result: ______________________________
Public link, if permitted: ______________________________

Education and Relevant Training

Degree or completed training: ______________________________
Institution or provider: ______________________________
Completion year, if useful: ______________________________

Completed resume

Fictional example for learning purposes.

Jordan Lee
Raleigh, NC | 202-555-0148 | jordan.lee@northstarmail.test

Professional Profile

AI evaluation analyst focused on the quality of retrieval-augmented support assistants and tool-using workflows. Designs rubric-scored case sets, checks automated scores against human review, and turns trace-level failures into clear recommendations for product and engineering teams. Experienced in Python, SQL, versioned evaluation data and cross-functional reporting.

Core Skills

Evaluation design and rubric calibration; retrieval relevance and groundedness; agent task completion; case sampling and holdout control; human review and agreement checks; error analysis and regression testing; Python, SQL, Git and MLflow; scorecards, finding reports and engineering handoffs.

Professional Experience

AI Evaluation Analyst | Northstar Service Systems | Remote, United States | May 2023–Present

• Built and maintained a 220-case evaluation set across seven support workflows, with documented case provenance, rubric definitions and a protected holdout for release comparisons.
• Calibrated five reviewers on 60 paired assistant responses and documented disagreement rules; agreement on that scored sample rose from 72% to 86% over two review cycles.
• Used Python and SQL to compare answer correctness, retrieval relevance and groundedness across model and prompt versions; tracked runs and dataset versions in MLflow and Git, then published a scorecard with error slices and measurement limits for the product owner.
• Reproduced 46 retrieval and tool-use failures from approved traces, grouped likely causes, and partnered with engineering on retests; 40 cases passed the next regression run and six remained open with owners.
• Performed approved prompt-injection checks in a staging environment, recorded reproducible failures, and escalated findings to the security lead for disposition.

Quality Analyst | Harborline Analytics | Durham, NC | June 2020–April 2023

• Maintained a regression checklist for customer-support automation and linked each failed case to the affected workflow, expected behavior and ticket owner.
• Built weekly SQL summaries of recurring answer and routing errors, helping product staff prioritize fixes against customer-impact evidence.
• Wrote concise reviewer guidance and coached new analysts on consistent scoring and escalation of ambiguous cases.

Selected Evaluation Work

Support Assistant Evaluation Pack | 2024
Designed a shareable, non-customer case set and scoring guide for answer grounding, retrieval relevance and task completion; documented sampling limits and a review process for disputed scores.

Education

Bachelor of Science in Statistics | University of North Carolina at Greensboro | 2020

Tailor and check before applying

1. Read the exact posting. Identify the system, quality dimensions, expected outputs, level and any required qualifications. Separate required items from preferred items.
2. Keep only evidence you can defend. Replace the blank template fields with your own facts, remove unused sections, and ensure every named tool appears in work you actually performed.
3. Reorder your strongest relevant bullets so the first third of the resume answers the employer's main evaluation need. Use the posting's terminology naturally where it matches your experience.
4. Check accuracy and boundaries. Confirm dates, role titles, degree names, metrics, denominators, employer permissions and whether you recommended or actually owned a decision. Remove restricted case content and unapproved red-team details.
5. Export in the format the application accepts. Select and copy text from the exported file, read it in order, then review the application fields after upload and correct parsing errors.