AI trainer & data annotator

What does an AI trainer & data annotator do?

An AI trainer and data annotator prepares the data that machine learning models learn from.

You label text, images, audio or video. You also write and rate model responses and check them against written guidelines.

In Singapore, the role sits in data, AI or product teams at banks, technology firms, healthcare providers, public agencies and annotation vendors. Many roles are project based. Some are entry points into wider AI work.

You work with machine learning engineers, data scientists and product managers. You also work with team leads who write labelling rules and review quality, and with subject experts who settle hard cases.

What the work involves

• Label or tag text, images, audio or video by following the project guidelines.
• Rate and compare AI model responses for accuracy, tone and safety, and write short reasons.
• Write prompts and ideal answers that the team uses to train or test a model.
• Check labelled data for errors, gaps and duplicates using Excel or simple Python scripts.
• Flag unclear guidelines and tricky cases to the team lead, and record how each one was settled.
• Report model failures, such as wrong or unsafe answers, with the exact prompt and output.

Pay in Singapore

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Skills an AI trainer & data annotator needs

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AI trainer & data annotator job description

We are hiring an AI trainer and data annotator to prepare and check the data our models learn from.

You will label data, rate model outputs and write training prompts in line with project guidelines. You will work with engineers and data teams in Singapore, and your accuracy and consistency will decide how well our models perform.

Responsibilities

• Label text, image, audio or video data to the accuracy and speed set for the project.
• Follow and give feedback on labelling guidelines, including edge cases and tie-break rules.
• Write, test and refine prompts for models such as ChatGPT, and write model answers for training.
• Rate and rank model responses against a rubric covering accuracy, helpfulness and safety.
• Review other annotators' work and report error rates to the team lead.
• Clean and check datasets for duplicates, missing labels and uneven class counts.
• Log model failures with the prompt, the output and a short description of what went wrong.
• Handle personal data as the Personal Data Protection Act (PDPA) and company policy require, including masking where needed.
• Meet daily or weekly targets for quality and volume, and report delays early.

Requirements

• Diploma or degree in any field is common. Language, linguistics, science or domain degrees help on specialist projects.
• 0 to 2 years of experience is typical. Data entry, content moderation, research or customer support work all count.
• Strong written English. Mandarin, Malay or Tamil is useful for multilingual projects.
• Working skill with Excel, and basic Python for checking data, is often asked for.
• Familiarity with ChatGPT or similar tools, and a clear sense of what language models get wrong.
• Close attention to detail and the patience to apply the same rules thousands of times.

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AI trainer & data annotator career path

Most people start by labelling data and move into reviewing quality, then lead projects or teams.

Some then move into technical or governance roles in AI.

1. Data annotator

   Entry

   You label data and rate model outputs by following guidelines. Your work is checked by reviewers.

2. AI trainer

   Mid

   You write prompts and model answers, handle harder cases and give feedback on guidelines.

3. Annotation quality reviewer

   Senior

   You audit other annotators' work, measure agreement and coach people on common errors.

4. Annotation project lead

   Lead

   You write guidelines, plan batches, set quality targets and report progress to the AI team and clients.

5. Head of data operations

   Head

   You run annotation teams and vendors across projects, and set standards for data quality and data handling.

The titles are ones commonly used in Singapore; employers may name the levels differently.

Related jobs and their pay

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AI trainer & data annotator interview questions

We wrote these questions from the skills this job needs. They are not an official question bank.

1. How would you write labelling guidelines for sorting customer emails into a fixed set of categories?

   What a good answer covers

   • Defines each category with clear examples and non-examples
   • Sets rules for edge cases and ties
   • Includes an 'other' or 'unclear' label
   • Runs a small pilot batch and revises the guidelines
   • Keeps version notes so past labels can be traced

2. What is inter-annotator agreement, and how would you measure it?

   What a good answer covers

   • Describes how often annotators give the same label to the same item
   • Names a measure such as percent agreement, Cohen's kappa or Krippendorff's alpha
   • Treats low agreement as a sign of unclear guidelines
   • Uses a review step to settle disagreements
   • Audits a sample on a regular schedule

3. Explain precision and recall to a colleague who is not technical. How do label errors affect them?

   What a good answer covers

   • Gives a simple example, such as spam filtering
   • Precision is how many flagged items were right; recall is how many true items were found
   • Notes that wrong labels teach the model wrong patterns
   • Says errors in the test set also distort the scores

4. How would you check a labelled dataset for problems using Excel or Python?

   What a good answer covers

   • Looks for duplicates and missing labels
   • Counts labels per class to spot imbalance
   • Compares label counts and error rates per annotator
   • Uses pivot tables or pandas value_counts and groupby
   • Pulls a random sample for manual review

5. Write a prompt that gets ChatGPT to pull names and dates from invoices in a fixed format.

   What a good answer covers

   • States the task and the output format clearly, such as JSON with named fields
   • Includes one or two examples
   • Says what to return when a field is missing
   • Tests the prompt on varied invoices and records failures
   • Refines the wording based on the errors found

6. How do you rate two model answers to the same question?

   What a good answer covers

   • Uses the project rubric
   • Checks facts against a reliable source
   • Weighs accuracy, completeness, tone and safety
   • Judges quality, not length
   • Writes a short reason for the rating

7. What are the common weaknesses of large language models, and why do they matter for training data?

   What a good answer covers

   • Mentions made-up facts, bias and sensitivity to wording
   • Says models can be out of date
   • Explains that human-written examples and reviews reduce these problems
   • Notes that narrow or skewed data creates skewed behaviour

8. You must label a sentiment dataset. How do you decide between a quick spreadsheet process and a dedicated annotation tool?

   What a good answer covers

   • Considers data type, volume and number of annotators
   • Notes that spreadsheets suit small text tasks
   • Notes that tools give review queues, audit trails and agreement checks
   • Considers data access and security
   • Picks the simplest option that meets quality needs

1. You find that 8% of a labelled batch is wrong, and delivery is due tomorrow. What do you do?

   What a good answer covers

   • Stops adding to the problem and checks how widespread the errors are
   • Traces the cause, such as unclear rules or one annotator's habit
   • Relabels the highest-risk items first
   • Tells the lead with numbers and a proposed plan
   • Reports the problem openly even with the deadline close

2. Two experienced annotators keep labelling the same type of item differently, and your lead is away. What next?

   What a good answer covers

   • Collects example items where they disagree
   • Checks what the guidelines say
   • Brings both annotators together to explain their reasoning
   • Makes an interim decision and records it
   • Sends the case to the lead to confirm and update the guidelines

3. During testing, a model gives a confident but wrong answer to a health question. How do you report it?

   What a good answer covers

   • Saves the exact prompt and full response
   • Reproduces the problem more than once
   • Labels the failure type and how serious it is
   • Sends it through the agreed channel
   • Suggests a better answer that can be used as training data

1. Tell me about a time you kept your accuracy high on repetitive work.

   What a good answer covers

   • Describes a specific task and volume
   • Explains the checks or habits used, such as breaks or self-review
   • Gives a result such as a lower error rate
   • Shows how the habit was shared or kept up

1. Your text dataset has Singlish, Mandarin and Malay mixed with English. How do you label it consistently?

   What a good answer covers

   • Asks for guidance on how mixed-language text should be handled
   • Involves native or fluent reviewers for each language
   • Keeps a glossary of local terms and agreed meanings
   • Flags sarcasm and unclear tone instead of guessing
   • Updates guidelines as new cases appear

2. Customer messages in a bank chatbot dataset contain NRIC numbers and phone numbers. How do you handle them?

   What a good answer covers

   • Raises it with the data protection officer or lead before continuing
   • Masks or removes personal data where the task does not need it
   • Limits access and keeps the data off personal devices and tools
   • Follows the Personal Data Protection Act (PDPA) and company policy
   • Records what was done

Common questions about an AI trainer & data annotator job

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