United Kingdom · Technical roles · Entry Level (0-2 years)

Junior AI Data Annotator

Here is the whole job, in plain words. What it is, a real day, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

Experience bandEntry Level (0-2 years)
Direct reportsNo direct reports
Reports toSenior AI Data Assistant
UK framework levelUsually someone starting out, or keeping a process running

Also advertised as AI Data Labeller · Data Labelling Specialist · Junior Data Curator

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

Start with a free Future Fluency check, tuned to Junior AI Data Annotator

Ten quick questions, one per Future Fluency, asked against this role rather than a generic one. About five minutes, and no card.

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1What this role really is

This isn't just data entry; it's about building the foundational datasets that teach AI models to 'see' and 'understand' the world. You'll be the eyes and ears for our machine learning systems, turning raw information into precise, usable intelligence. It's a hands-on role where your keen eye directly impacts how well our AI performs.

2What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Labelbox / V7 (or similar annotation platforms)Intermediate

Executing various data labelling tasks (e.g., image segmentation, object detection, text classification) according to project guidelines.

Python (with pandas basics)Basic

Running pre-written scripts to perform simple data cleaning, reformatting, or validation checks. Making minor, guided modifications to existing scripts.

PostgreSQL (basic querying)Basic

Executing simple `SELECT`, `FROM`, `WHERE` queries to pull specific datasets for annotation or to verify data counts.

Git & GitHub/GitLabIntermediate

Cloning repositories, pulling latest changes, committing your own script modifications, and pushing them to the remote. Resolving simple merge conflicts with guidance.

Managing your assigned tasks, updating ticket statuses, logging bugs, and creating clear descriptions for issues or guideline queries.

AWS S3 / GCP Cloud StorageBasic

Accessing, downloading, and uploading labelled datasets to designated cloud storage buckets, understanding folder structures and basic access permissions.

3What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choice	Coming in	Where you are now	The step above
Annotation Guideline Interpretation	Follow existing guidelines strictly. Escalate any ambiguous 'edge cases' to Senior AI Data Assistant for clarification.	Interpret guidelines for common edge cases. Propose updates to guidelines for novel situations, seeking approval from Senior AI Data Assistant or ML Engineer.	Define and refine annotation guidelines for new projects. Make final decisions on complex edge cases, documenting rationale. Train junior annotators on guideline application.
Data Quality Issue Resolution	Identify and flag data quality issues (e.g., corrupt files, obvious errors) to Senior AI Data Assistant. Do not attempt to fix without explicit instruction.	Independently resolve common data quality issues (e.g., minor formatting, de-duplication) using established scripts. Escalate complex or systemic issues.	Design and implement data quality checks and validation processes. Lead the resolution of systemic data quality problems, collaborating with Data Engineering.
Tool/Platform Usage	Use assigned annotation platforms (e.g., Labelbox, V7) and specified scripts as instructed. Report any software bugs or usability issues.	Choose appropriate features within established tools to optimise workflow. Propose minor improvements to scripts or tool configurations.	Evaluate and recommend new annotation tools or features. Configure and customise platforms for specific project needs. Train the team on tool best practices.
Task Prioritisation	Work on tasks as prioritised by your Senior AI Data Assistant. If unsure, ask for clarification on urgency.	Manage your own daily task queue based on project deadlines and manager guidance. Escalate if workload becomes unmanageable.	Prioritise tasks for a small team or specific workstream. Negotiate deadlines with ML Engineers and Data Scientists, keeping your manager informed.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Labelling Throughput

The volume of data points or items you accurately label within a given timeframe.

Target · Typically >150 labels per hour, depending on task complexity.

If a task involves bounding boxes on images, you'd aim for 150-200 boxes per hour. For more complex semantic segmentation, it might be 50-80. We'll track your average over the week.

Annotation Accuracy

How closely your labels match the 'ground truth' or the agreed-upon standard, as verified by QA checks.

Target · >99% accuracy on QA checks.

Out of 1,000 labels reviewed by a Senior Annotator, you should have no more than 10 errors. Missing a crucial object or miscategorising something would count as an error.

Guideline Adherence Score

How well you interpret and apply the specific labelling instructions and taxonomies.

Target · Consistently score 4/5 or higher in guideline application reviews.

If the guideline says 'only label cars parked on the road', and you've labelled cars in a car park, that would be a guideline adherence issue. We're looking for consistent application, even in tricky edge cases.

Task Completion Rate

The percentage of assigned labelling tasks you complete within the given deadline.

Target · 95% of tasks completed on or before deadline.

If you're assigned five batches of data to label this week, we expect you to finish at least four and a half of them on time, giving us a heads-up if you hit blockers on the last one.

Proactive Issue Identification

You're not just labelling; you're spotting patterns, inconsistencies, or ambiguities in the data or guidelines and bringing them up.

Regularly flagging unclear instructions, proposing new edge cases, or pointing out data quality issues before they become bigger problems. You'll log these in Jira with clear screenshots and descriptions, rather than just silently making an assumption.

Learning and Adaptability

How quickly you pick up new annotation guidelines, tools, or feedback, and apply them to your work.

Reduced number of recurring errors after receiving feedback. Successfully taking on new, more complex annotation tasks. Actively asking clarifying questions during training sessions and applying the answers immediately.

Collaboration and Communication

Your ability to clearly communicate progress, blockers, and data insights to your Senior AI Data Assistant and the wider ML team.

Providing concise daily updates. Writing clear, actionable bug reports or guideline queries in Jira. Actively participating in team stand-ups, even if it's just to say 'no blockers today'.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy

Making a Tangible Impact

You'll feel a sense of satisfaction knowing that every accurate label you apply directly contributes to the 'brain' of our AI models. You're building the foundation, and you'll see the results in better product features.

When a new AI feature launches, you'll know your precise annotations helped make it possible. It's a direct line from your work to a real-world product.

Mastering Detail & Accuracy

If you genuinely enjoy the challenge of spotting tiny errors, following complex instructions perfectly, and delivering work that's essentially flawless, you'll find this role deeply satisfying. It's about craftsmanship in data.

You'll take pride in a batch of data that passes QA with zero errors, knowing you've maintained a consistently high standard over hundreds of items.

Learning & Growing in AI

This role is a fantastic entry point into the AI world. You'll learn the fundamentals of how ML models are trained, what 'good' data looks like, and the tools used in the industry. You're constantly absorbing knowledge by doing.

You'll learn about different annotation types, how data quality impacts model performance, and get hands-on with tools like Labelbox and Python, setting you up for future technical roles.

What frustrates people

The Monotony Grind: Spending an entire day drawing bounding boxes around cars or highlighting text can be mentally exhausting. It demands immense focus and can feel repetitive.
Ambiguous Guidelines: Sometimes, the instructions you get aren't crystal clear, especially for weird 'edge cases'. You'll have to ask questions, wait for answers, and sometimes still feel like you're guessing, which can be frustrating.
The 'Cog in the Machine' Feeling: You might occasionally feel like 'just a labeler' rather than a critical technical contributor, even though your work is the absolute foundation of the AI model.
The Dreaded Re-Work: Imagine spending a week meticulously labelling thousands of items, only to be told a core assumption was wrong, and you need to start almost from scratch. It happens, and it's tough.
Tooling Nightmares: Sometimes the annotation software can be buggy, slow, or just not quite right for the task, leading to crashes and lost work. It's a real pain when you're trying to be efficient.
Quality vs. Speed Paradox: You'll often be pushed to label faster, but also held to near-perfect accuracy. Balancing these two can be a constant, tricky challenge.

What this role does not give you

High-level strategic decision-making (that comes later).
Constant variety in tasks (some days are very similar).
Direct interaction with external clients (mostly internal focus).
Immediate gratification from seeing your code deployed (your impact is more foundational).

6Who you work with

Your work is the bedrock of our AI development. Without accurately labelled data, our machine learning models simply don't learn correctly. You're essentially teaching the AI, which means your precision directly influences the quality of our products and our ability to innovate. Get it right, and our AI is brilliant; get it wrong, and we're back to square one, wasting valuable engineering time.

Inside the business

Senior AI Data Assistant (your direct manager)
Machine Learning Engineers (who use your data)
Data Scientists (who design experiments)
Product Managers (who define what the AI needs to do)

7What you need before you start

Not a wish list. The things you would be expected to already have.

A proven track record (even in non-work settings) of exceptional attention to detail and accuracy.
The ability to follow complex, multi-step instructions precisely and consistently.
Basic computer literacy, including proficiency with common office software (e.g., spreadsheets, word processors).
A genuine interest in technology, particularly AI and machine learning, and a desire to learn how it's built.
The ability to communicate clearly, both verbally and in writing, especially when describing issues or asking questions.
A proactive approach to learning and problem-solving, trying to figure things out before asking for help.

8What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Scripting for Data Pre-processing

As you progress, you'll need to do more than just run existing scripts. You'll start writing your own, more complex Python scripts to automate data cleaning, transformation, and validation tasks, especially for new or messy datasets.

Pandas DataFrames · Error Handling · Modular Code Design

This week: Take an online course on intermediate Python for data analysis (e.g., using DataCamp or Coursera).
This month: Try to write a small script to automate a repetitive data cleaning task you currently do manually.
Month 2: Get your Senior AI Data Assistant to review your script and give you feedback on best practices.
Month 3: Contribute a small, useful script to the team's shared codebase.

Quick win: Ask to pair-program with a Senior Annotator or ML Engineer for an hour to see how they approach script writing for data tasks.

9Staying current once you are in

What people here do to keep up

Regularly engage with online courses (e.g., Coursera, Udacity) on Python, SQL, or machine learning fundamentals.
Participate in Kaggle competitions or personal data labelling projects to hone your skills and explore new data types.
Attend webinars or online meetups focused on AI data quality, annotation best practices, or specific annotation tools.
Read industry blogs and articles (e.g., Medium, Towards Data Science) to stay updated on emerging trends in AI and data.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Basic Prompt Engineering for Annotation

Large Language Models (LLMs) and other generative AI tools are becoming incredibly powerful. Knowing how to 'talk' to them effectively—writing good prompts—will be key to using them for pre-labelling, data summarisation, or even generating synthetic data for annotation.

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Basic Prompt Engineering for Annotation

Large Language Models (LLMs) and other generative AI tools are becoming incredibly powerful. Knowing how to 'talk' to them effectively—writing good prompts—will be key to using them for pre-labelling, data summarisation, or even generating synthetic data for annotation.

Clear Instruction Crafting
Context Provision
Output Validation
Iterative Prompt Refinement

Basic Data Analysis for QA

As datasets grow, manual QA becomes impossible. You'll need to move beyond just spotting individual errors to understanding basic statistical patterns in your labelled data to identify systemic issues. This helps you work smarter, not just harder.

Frequency Distributions
Basic Visualisation
Outlier Detection (Conceptual)
Inter-Annotator Agreement (IAA) Interpretation

What you’ll use

Skills this role draws on

Technical

Data Annotation Methodologies
Data Quality Assurance (QA) Principles
Data Curation & Cleansing Basics
Taxonomy & Ontology Application
Understanding of the ML Lifecycle (Contextual)

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

1
Recent Graduate (Technical/Numerate Degree)
0-1 years
Skills to master
- Applying academic rigour to real-world, messy data
- translating theoretical knowledge into practical annotation skills
- learning industry-specific tools and workflows.
You're ready to move on when
- Demonstrable projects (university or personal) involving data analysis or programming.
- Strong academic performance in subjects requiring precision and logical thinking.
- A clear enthusiasm for AI and a willingness to learn the practicalities of data preparation.
2
Career Changer (from detail-oriented roles)
1-2 years
Skills to master
- Transferring existing precision and meticulousness (e.g., from quality control, administrative roles, design) to the specific demands of data annotation
- picking up technical tools like Python and SQL.
You're ready to move on when
- A history of roles where accuracy and adherence to standards were critical.
- Evidence of self-study or certifications in basic data skills (e.g., Python, SQL).
- A clear narrative for why you want to transition into the AI/tech space.
3
Internship/Apprenticeship Programme
0-1 years
Skills to master
- Gaining hands-on experience with annotation platforms and guidelines
- understanding team dynamics and project workflows
- building confidence in a professional technical environment.
You're ready to move on when
- Successful completion of a relevant internship or apprenticeship.
- Positive feedback from supervisors on attention to detail and work ethic.
- A portfolio of small projects or tasks completed during the programme.

11Where this role leads

The long view:Your journey starts here, building the very foundation of artificial intelligence. It's a challenging but incredibly rewarding path, and we're excited to see where your precision and passion for data will take you.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Junior AI Data Annotator is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Machine Learning Methods and Models in Data ScienceLevel 3

Applied to your work in Junior AI Data Annotator

The objective of this unit is to provide learners with a foundational understanding of machine learning methods and models used in data science. Learners will gain knowledge of supervised, unsupervised, and reinforcement learning, including their applications and key characteristics.

How the thinking builds

Remember
Understand
Apply
Analyse
Evaluate
Create

An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Junior AI Data Annotator

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

Labelling ThroughputThe volume of data points or items you accurately label within a given timeframe.If a task involves bounding boxes on images, you'd aim for 150-200 boxes per hour. For more complex semantic segmentation, it might be 50-80. We'll track your average over the week.Typically >150 labels per hour, depending on task complexity.
Annotation AccuracyHow closely your labels match the 'ground truth' or the agreed-upon standard, as verified by QA checks.Out of 1,000 labels reviewed by a Senior Annotator, you should have no more than 10 errors. Missing a crucial object or miscategorising something would count as an error.>99% accuracy on QA checks.
Guideline Adherence ScoreHow well you interpret and apply the specific labelling instructions and taxonomies.If the guideline says 'only label cars parked on the road', and you've labelled cars in a car park, that would be a guideline adherence issue. We're looking for consistent application, even in tricky edge cases.Consistently score 4/5 or higher in guideline application reviews.
Task Completion RateThe percentage of assigned labelling tasks you complete within the given deadline.If you're assigned five batches of data to label this week, we expect you to finish at least four and a half of them on time, giving us a heads-up if you hit blockers on the last one.95% of tasks completed on or before deadline.

These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Junior AI Data Annotator to AI Data Assistant (Level 2), and whatever you decide comes after.

Level 2 · in progressAI Fluency→ AI Data Assistant (Level 2)→ your design

Where this takes you

Your journey starts here, building the very foundation of artificial intelligence. It's a challenging but incredibly rewarding path, and we're excited to see where your precision and passion for data will take you.

See Your Progress GrowIllustration

Junior AI Data Annotator

Data Annotation Methodologies
Data Quality Assurance (QA) Principles
Data Curation & Cleansing Basics
Taxonomy & Ontology Application
Understanding of the ML Lifecycle (Contextual)

This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

14The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Junior AI Data Annotator is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

AI Data Assistant (Level 2)
2-3 years in current role
You'll move from executing tasks under close supervision to owning complete batches of data, handling common edge cases independently, and contributing to minor guideline refinements.
- Advanced Python Scripting: Writing simple scripts for data validation and transformation.
- Complex SQL Querying: Using `JOIN`s and `GROUP BY` to create custom datasets.
- Basic Data Version Control (e.g., DVC): Tracking changes to datasets.
- Understanding of Inter-Annotator Agreement (IAA) metrics.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, data annotation can be a bit of a grind. But what if you could cut down the repetitive parts, leaving you more time for the interesting bits and boosting your accuracy? That's exactly what AI-powered tools can do for you in this role.

In the world of AI data annotation, it's not about replacing humans; it's about augmenting them. Imagine having an intelligent co-pilot that handles the grunt work, flags potential errors, and helps you learn faster. That's the reality we're building here. You'll be using AI to make your own job easier and more impactful, right from day one.

Automated Pre-Labelling

Instead of starting from scratch, a base AI model will do a first pass on your data. Your job then shifts to correcting its mistakes, which is often 2-3 times faster than manual labelling. Think of it as having a rough draft that you just need to polish.

AI-Powered Anomaly Detection

Imagine an AI tool scanning your newly labelled dataset and automatically flagging anything that looks statistically odd – like a bounding box that's way too big or a text label that doesn't fit the pattern. It points you straight to potential errors, saving you hours of manual QA.

Contextual Research Assistant

When you encounter highly specialised data or jargon (say, medical terms or obscure legal phrases), you can use a fine-tuned LLM as an instant research assistant. It'll quickly define terms or provide context, so you don't have to spend ages searching external resources or waiting for an SME.

Guideline & Report Drafting

After you've identified a tricky new 'edge case', you can use an LLM to help draft a clear, concise update to the official labelling guidelines. You can also use it to summarise your daily progress or draft quick reports on QA findings, turning numbers into understandable insights faster.

Common questions

How do you become a Junior AI Data Annotator?

Common routes in include Recent Graduate (Technical/Numerate Degree) (0-1 years), Career Changer (from detail-oriented roles) (1-2 years) and Internship/Apprenticeship Programme (0-1 years). Times vary with prior experience.

Where can a Junior AI Data Annotator progress to?

This role can lead on to AI Data Assistant (Level 2) (2-3 years in current role), depending on the skills you build.

What level is a Junior AI Data Annotator in the UK?

This role aligns to RQF Level 2 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for a Junior AI Data Annotator?

Increasingly, Basic Prompt Engineering for Annotation and Basic Data Analysis for QA. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows a Junior AI Data Annotator, works on the job you actually do, and keeps going at your pace rather than a timetable's.

Searching and planning stay free. You only pay when you start learning.
Your credits are yours. Regulated, and they don't vanish when a subscription ends.
Cancel any time and billing stops. No notice period, no minimum term.

Your path, personalised

You have the map. Walking it is the part we do together.

This route runs to 12 national skill standards. That is a real journey.

Zavmo shapes a learning experience as unique as you are. It fits how you learn, your pace and the work you already do. Every step stays benchmarked to recognised national standards. That’s the plan for becoming a Junior AI Data Annotator: personal to you, and it still counts. The first steps are free.

Independent research finds well-designed intelligent tutoring performs nearly as well as one-to-one human tutoring: VanLehn (2011), Educational Psychologist.

A private tutor in the UK averages £35–40 an hour . Zavmo is £70/month.

A Zavmo curriculum screen: a three-month plan built for a customer support lead, five Ofqual-regulated units and a hundred and ninety-six credits. — A real plan on learn.zavmo.ai: Ofqual-regulated units, credits, and a three-month run at your own pace.

Start free No commitment. See your first steps free.

15Where to go from here

Other roles at Level 2

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

Other roles in Technical roles

Stay in the field you know and move sideways rather than up.

If you leave this industry

The skills you'll build here—precision, data literacy, technical tool usage, and an understanding of AI fundamentals—are highly transferable. You could move into broader Data Analyst roles, Data Quality Engineering, or even specialise further in specific domains like Computer Vision or Natural Language Processing within other tech companies.

Not sure this is the right direction?

Work out what you actually want from work first, then come back and see which roles fit it. Takes about ten minutes.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.

Junior AI Data Annotator

What's rising in its place

Basic Prompt Engineering for Annotation

Basic Data Analysis for QA

Skills this role draws on

Technical

How you actually get there, here

Recent Graduate (Technical/Numerate Degree)

Career Changer (from detail-oriented roles)

Internship/Apprenticeship Programme

The credential is what you can do today. These are what keep you valuable.

Roles this leads to

No two people are taught the same way. This is one-to-one, not one-to-many.

A conversation, not a course

No two people run this the same way

This isn't a certificate you file away. It's a passport to the life you're designing.

Your journey starts here, building the very foundation of artificial intelligence. It's a challenging but incredibly rewarding path, and we're excited to see where your precision and passion for data will take you.

Everything else the record holds

The career path, and where it branches

AI Data Assistant (Level 2)

Where AI is starting to help

Automated Pre-Labelling

AI-Powered Anomaly Detection

Contextual Research Assistant

Guideline & Report Drafting

Common questions

You’ve started things before

Less than one coaching session. Every month.

You have the map. Walking it is the part we do together.