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AI evaluation analyst job description template

Use this fresher AI evaluation analyst job description for work rating AI text answers against written guidelines. It includes an intern version, screening questions and guidance on stating languages, shifts and potentially distressing content.

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In short

A fresher AI evaluation analyst rates the answers an AI model gives against written guidelines, checks claims against sources, labels data and records the errors that repeat, while a senior reviews the ratings. Ask for close reading, clear writing in English, sound judgement, spreadsheet skill and honesty about doubt. Basic Python is an extra, as is another Indian language unless the post rates answers in it.

AI evaluation analyst job description template

Fill in your company details once and both versions below update: the full-time job for freshers and the internship. Copy the one you need into your job post.

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1. Full-time AI evaluation analyst (fresher)

AI evaluation analyst (fresher)

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Full-time
CTC: [CTC for the full-time role]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is hiring a fresher AI evaluation analyst for the [Team] team. You will rate AI answers against written guidelines, check claims against sources and record recurring errors. You will help maintain test questions so the team can compare results after changes. A senior reviews your ratings. Content reviewed: [ordinary product text only / describe potentially distressing content]. Support arrangements: [state available breaks, the escalation contact and how to hand back a batch, where relevant].

What you will do

  • Read the written guidelines and their worked examples before each batch, and ask the guideline owner when a case is not covered
  • Rate [chatbot answers / search results and summaries / translations (add the languages to the requirements) / extracted fields] on the scale in the guidelines, and write a one- or two-line reason for each rating
  • Compare each factual claim with the source supplied in the task, or with the source the answer cites, and mark it supported, unsupported or contradicted
  • Write test prompts for a feature: plain requests, vague ones, ones outside its remit and ones built to trip it up, then run them only on the test version named in the task
  • Label and check text data against the labelling rules, and re-check a sample of your own labels the next day to see whether you still agree with them
  • Log each error in the shared sheet with its type, such as an invented fact, a wrong figure, an ignored instruction or a mixed-up source, and keep one example per type for the engineers
  • Help maintain the team’s fixed set of questions with known-good answers: add new cases, retire outdated ones and rerun the set after each change to the model or its instructions, recording the result in the tracker
  • Work through a shared sample with a second reviewer, list the cases where your ratings differ and bring them to the weekly calibration discussion
  • Flag guidelines that are unclear, clash with one another or miss a case, and quote the example that showed the gap
  • Use only the fictional or approved data in the task, and tell your senior at once if a batch holds real customer details or upsetting material you were not told about

What we are looking for

  • Strong reading and writing in English: can read a long answer closely and explain a rating in one or two clear lines
  • Careful judgement: applies a written guideline to each case in the same way, and says when a case does not fit it
  • Spreadsheet skill in Excel or Google Sheets: filters, sorting, simple formulas and counts, and a tidy results sheet
  • Honesty about uncertainty: writes ‘not sure’ with a reason and what would settle it, in place of guessing to finish the sheet
  • The habit of checking a claim against its source, such as opening the page an answer cites and finding the sentence
  • Patience for detailed, repeated checking, and the discipline to keep the same standard from the first item to the last
  • An example of careful checking that you can describe or show, such as proofreading, fact-checking, marking a test or labelling data, from study, a project or voluntary work

Good to have

  • Basic Python or SQL for opening data files and counting results
  • Reading and writing Hindi or another Indian language, for checking answers given in it [move to requirements if the post rates answers in it]
  • Knowledge of the subject the product covers, such as health, finance, education or software
  • Time spent with a labelling or review tool, for example Label Studio, or in proofreading and fact-checking work
  • A plain-language idea of what a large language model is and why it can state a wrong answer in a confident tone

Who can apply

Final-year students and graduates from any stream can apply, including BA, B.Com, B.Sc, BBA, BCA and B.Tech. Careful reading, clear writing and sound judgement matter more than the degree, and knowledge of the subject our product covers is a bonus.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

2. AI evaluation analyst internship

AI evaluation analyst intern

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Internship · Duration: [Internship duration]
Stipend: [Stipend for the internship]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is looking for an AI evaluation intern to rate and check model answers with a mentor, using fictional or approved material only. You will learn the guidelines, rate practice batches, record what goes wrong and help extend a set of test questions. If you wish to publish your work or show it in a portfolio, [Company name] has to approve it first.

What you will do

  • Rate a practice batch of fictional answers, then compare your ratings with your mentor’s and write down each difference and why it happened
  • Check ten answers against a one-page source and mark each claim supported, unsupported or contradicted, with your mentor reading the result
  • Write a set of test questions on one topic, each with the answer it should get and the page that proves it
  • Group errors from a practice batch by type, count the items in each group and show your mentor one example of the most frequent error.
  • Rewrite vague test questions so that a reviewer could mark the reply right or wrong, and show the before and after
  • Note every place where the guidelines were unclear to you and suggest clearer wording with an example
  • Present a short write-up of what you found to the team at the end

What we are looking for

  • A final-year student or recent graduate, in any stream
  • Reads English closely and can explain a judgement in two or three clear sentences
  • Can sort, filter and count in a spreadsheet
  • Willing to follow written guidelines and to write ‘not sure’ when a case is unclear
  • Can describe one time you checked work or facts carefully, such as proofreading, marking a test or comparing a claim with its source

Who can apply

Final-year students and graduates from any stream can apply, including BA, B.Com, B.Sc, BBA, BCA and B.Tech. Careful reading, clear writing and sound judgement matter more than the degree, and knowledge of the subject our product covers is a bonus.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

Other titles for this role

Junior AI evaluation analyst, LLM evaluator (fresher), AI quality analyst, AI trainer (ratings and labels), Data quality analyst (AI).

Screening questions to add to your job post

  1. An AI answer sounds confident, but the source it cites does not say that. What do you do? Look for opening the source, finding the sentence or its absence, marking the claim unsupported and noting the type of error. Accept a written note or a flag in a tool. The aim is that nothing passes because it sounds right.
  2. Two reviewers rate the same answer differently. How do you settle it? Look for going back to the guideline wording first, each person giving a reason, and a question to the guideline owner when the text does not decide the case. Accept a short call or a written exchange.
  3. What makes an evaluation guideline unclear? Give one example from a marking scheme, rubric or checklist you have used. Look for words open to different readings, such as ‘good’ or ‘helpful’, a case the guideline misses, or two rules that clash. Accept an example from coursework, a quiz key or any other checklist.
  4. A fictional policy says, ‘Employees need their manager’s written approval before booking leave.’ Rewrite ‘Is the leave policy good?’ as one question whose answer can be checked against that sentence. Look for a question such as ‘Whose written approval is needed before booking leave?’ Accept any clear question answerable from the supplied sentence, without invented policy details.
  5. You cannot tell whether an answer is right. What do you put in the sheet? Look for ‘not sure’ with the reason and what would settle it, such as a source or a subject expert. Accept flagging the item for a senior’s review.
  6. An answer is polite and well written but gets a date wrong. How do you rate it, and what reason do you write? Look for correctness judged apart from style, and a reason that names the error. Accept any scale, as long as the reason says which part earned which score.
  7. In a sheet of rated answers, how would you count the errors by type? Look for an error-type column and a filter, pivot table or count formula. Accept any equivalent method.

What to look for when you shortlist

  • Reasons that point to the guideline or the source, such as ‘the policy page says seven days and the answer says ten’
  • A willingness to write ‘not sure’ and to say what would settle the doubt
  • Short, plain writing that a stranger could follow without asking what was meant
  • Consistent application of the guidelines, with a clear reason when a rating changes and a flag when the rules do not settle a case
  • An eye for patterns, such as noticing that several wrong answers share one cause
  • Extra evidence, never a requirement: a small sheet in which the candidate rated answers from a chatbot or a classmate with a reason for each, shown or described if private

Mistakes to avoid in this job description

  • Posting ‘AI trainer’ work without saying what is rated: the title can also mean someone who teaches AI courses, so name the material (answers, labels, chats or summaries), the languages, the guidelines, the person who reviews the ratings, the hours and shifts, and who provides the laptop and internet, so candidates know the job before they apply.
  • Leaving out that some material may be upsetting: say so plainly in the post, rotate that work between people, name the support on offer, such as breaks and a person to talk to, and repeat it at interview, before any offer.
  • Judging reviewers by the number of items they rate in an hour: compare their ratings with a senior’s on a shared sample, read the reasons they write, and use speed only to plan workload.
  • Handing over guidelines with no worked examples and then marking people down for disagreeing: write examples for the hard cases, hold a calibration session, and treat repeated disagreement between careful reviewers as a gap in the guideline.
  • Using customer chats or records in hiring tests: give candidates fictional examples with no customer details, keep your real backlog out of the exercise and share the marking criteria beforehand.

Questions about hiring an AI evaluation analyst

What should a fresher AI evaluation analyst know?

Close reading and clear writing in English, the habit of applying a written guideline the same way each time, spreadsheet skills and honesty about doubt. Basic Python and knowledge of the subject are extras that help, and so is another Indian language unless the post rates answers in it. How your models behave can be taught on the job. A candidate who gives a reason for every rating is already doing the work.

How is an AI evaluation analyst different from a generative AI engineer, a QA engineer or a data analyst?

A generative AI engineer builds the feature, while the analyst judges its answers. A QA engineer tests whether software behaves as specified. An analyst rates text against written guidelines: a factual claim can be supported, unsupported or contradicted by a source, while qualities such as tone have no single right wording. A data analyst reports on business data. The jobs overlap, so list the duties. This page is for people who rate text answers; automated scoring, content moderation and image or speech review need different duties.

Does a fresher need Python, machine learning or a second Indian language?

No, not for the core of the job. Reading, judgement and spreadsheets carry it. Machine learning is not needed, and basic Python only helps with opening data files and counting results. If the job includes rating answers in Hindi or another language, name that language and those tasks in the post and list the language as a requirement for them. Otherwise list it as good to have. A candidate who reads only English can still do the English-language work.

How do I check that two fresher reviewers rate the same way?

Give both the same small batch of fictional answers and ask for a rating and a reason for each. Compare them case by case and discuss every difference against the guideline wording. Track agreement by listing the cases where the two ratings match and the cases that split them. When careful reviewers differ, the guideline may have a gap, so fix the wording before judging either person.

Should the post say if the work includes upsetting content?

Yes. Say so in the post, so that candidates know before they apply. If a batch can include violent, hateful or distressing text, say what kinds, rotate that work between people, allow breaks and a way to hand a batch back, and name the person or service candidates can turn to. Repeat it at interview, before any offer. Telling candidates early lets them decide for themselves.

What project suits an AI evaluation intern?

Choose one topic and one AI feature, such as an assistant answering questions from a fictional or public policy document. The intern rates a practice batch, builds a small set of test questions, each with the answer it should get, sorts the errors and presents what they found. Add a mentor and a weekly review. Posting the internship on LineupX is free, with no hidden charges, and you can add screening questions drawn from this page.

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