UK

United Kingdom · Technology and data · Mid-Level (2-5 years)

Machine Learning Engineer

Here is the whole job, in plain words. What it is, 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.

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Free to start: a plan built for this role, and a Future Fluency module to try first. No card.

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One email: what AI is changing in this job, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

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  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior Machine Learning Engineer or Lead Machine Learning Scientist
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as ML Developer · AI Engineer · Data Scientist (ML Focus) · Applied Scientist

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 2 of 4 standards
Qualifi Level 3 Diploma in Data Science Qualifi Ltd · Level 3 · covers 2 of 4 standards
OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence OCN London · Level 5 · covers 2 of 4 standards
Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 2 of 4 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 2 of 4 standards
ATHE Level 5 Extended Diploma in Computing ATHE Ltd · Level 5 · covers 2 of 4 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Machine Learning Methods and Models in Data Science Qualifi Level 3 Extended Diploma in IT and Data Science
Machine Learning Algorithms OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence
Machine Learning Pearson BTEC Level 5 Higher National Diploma in Cloud Computing
Data Analytics and Machine Learning ATHE Level 5 Extended Diploma in Computing

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
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Your Plan Illustration

Built for Machine Learning Engineer

4 units that map to this job, from the qualifications that cover it.

  1. Machine Learning Methods and Models in Data Science Qualifi Ltd · covers 2 of 4 standards Level 3
  2. Machine Learning Algorithms OCN London · covers 2 of 4 standards Level 5
  3. Machine Learning Pearson Education Ltd · covers 2 of 4 standards Level 5
  4. Data Analytics and Machine Learning ATHE Ltd · covers 2 of 4 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Machine Learning Engineer?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration
  • Responsible AI Practices (Bias & Fairness)
Why each one is rising

The future skills this job leans on

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

What happens when you start

Next, a free account. We build your Machine Learning Engineer plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

A machine learning engineer takes models from concept to production, making sure they work and deliver value in practice.

4What you'd actually use

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

Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) Advanced

This is your bread and butter. You'll use it for data manipulation, cleaning, model building, and scripting. We expect you to be comfortable with at least one deep learning framework (PyTorch or TensorFlow).

AWS SageMaker or GCP Vertex AI Intermediate

You'll be executing training jobs, deploying model endpoints, and monitoring them within one of these cloud ML platforms. You don't need to be an architect, but you should know your way around.

Databricks or Snowflake Intermediate

Used for large-scale data cleaning, transformation, and feature engineering. You'll be writing SQL and Python notebooks to prepare data for your models.

MLflow or Weights & Biases Intermediate

For tracking your experiments, logging model parameters, metrics, and artifacts. It's crucial for reproducibility and comparing different model runs.

Apache Airflow or Kubeflow Intermediate

You'll be running and monitoring existing data and ML pipelines (DAGs). You should be able to debug issues if a pipeline fails, and maybe even make small modifications to existing DAGs.

Jira & Confluence Intermediate

For managing your tasks, tracking project progress, and documenting your work, model designs, and technical decisions. It's how we keep organised.

5What 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 choiceComing in Where you are nowThe step above
Technical Approach (within project) Propose options, seek guidance and approval from Senior ML Engineer. Independently choose the best algorithm, feature engineering, or model validation technique within established guidelines. Escalate if it's a novel problem. Define the technical approach for entire workstreams, including major architectural choices. Consult with Lead/Staff on cross-project implications.
Data Sourcing & Preparation Use pre-defined datasets, request access to new data with guidance. Identify and request new data sources. Independently perform complex data cleaning and feature engineering. Escalate if data quality is a major blocker. Design and implement new data pipelines for ML features. Define data governance standards for ML projects.
Model Deployment & Monitoring Execute deployment steps using existing CI/CD pipelines under supervision. Monitor pre-defined dashboards. Independently deploy models using established MLOps pipelines. Set up and maintain model monitoring dashboards. Troubleshoot production issues. Design and build new CI/CD/CT pipelines. Define enterprise-wide model monitoring strategies and incident response plans.
Project Prioritisation (your tasks) Execute tasks as prioritised by your supervisor. Escalate conflicts. Prioritise your own tasks within a sprint to meet project goals. Proactively communicate any potential delays or conflicts to your lead. Prioritise tasks for a small team or workstream. Negotiate priorities with Product Managers and other stakeholders.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Model Performance (Key Metrics)
How well your deployed models are actually performing against their specific objectives, whether that's accuracy, recall, F1-score, or a custom business metric.
Target · Achieve >90% of target performance metrics (e.g., 92% recall for fraud detection, 90% accuracy for content classification).

Your new churn prediction model hits 93% recall on new customer data, exceeding the 90% target, meaning we're catching more at-risk customers.

Code Quality & Maintainability
The cleanliness, readability, and robustness of the code you write, including adherence to coding standards and minimal bug introduction.
Target · Maintain a bug rate of <5% in committed code, and receive positive feedback on code review comments (e.g., 'easy to understand', 'well-tested').

In your last 10 feature branches, only 2 minor bugs were identified post-merge, and your code consistently passes automated linting and testing.

Project Delivery & Timeliness
Your ability to complete assigned tasks and project segments within agreed-upon timelines and sprint estimates.
Target · Complete 90% of assigned Jira tickets within the sprint estimate, or proactively communicate delays with clear reasons.

You successfully delivered 9 out of 10 tickets in the last sprint, and the one delayed ticket had a clear explanation and revised estimate communicated early.

Model Inference Latency
The speed at which your deployed models can make predictions, which is crucial for real-time applications.
Target · Ensure deployed model APIs maintain a p99 inference latency of <100ms for routine operations.

The recommendation engine you deployed consistently responds within 80ms for 99% of requests, even during peak traffic.

Proactive Problem Solving
Your knack for not just identifying issues but also digging in to find potential solutions or workarounds before escalating.
  • You're the person who comes to your lead with 'Here's the problem, and here are three things I've tried/thought of.' You often spot potential issues in data or model behaviour before they become critical. You're not afraid to get your hands dirty debugging a tricky pipeline.
Effective Collaboration & Communication
How well you work with other teams (Product, Data Engineering, Software) and articulate complex technical concepts clearly to non-technical folks.
  • Product Managers consistently say you're easy to work with and understand their needs. Data Engineers find your data requests clear and well-defined. You can explain 'what a transformer model does' to the Head of Marketing without them glazing over. Your documentation is actually useful to others.
Continuous Learning & Self-Improvement
Your drive to stay current with the ever-evolving ML landscape, pick up new tools, and improve your craft.
  • You're often sharing interesting articles or new library releases with the team. You've picked up a new framework for a project without much hand-holding. You actively seek out feedback on your code and model designs to get better.
Ownership & Accountability
Taking responsibility for your work, from initial design to deployment and ongoing monitoring, and learning from mistakes.
  • When a model you built has a hiccup in production, you're the first to jump in and figure out what went wrong, rather than waiting to be told. You follow through on your commitments. You're honest about challenges and ask for help when needed, but you've already done your homework.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Building Things That Work

You get a real buzz from seeing your code go into production and knowing your model is actively making a difference. That feeling when a new recommendation engine goes live and you see the engagement metrics tick up? That's your fuel.

Spending an afternoon debugging a tricky deployment issue, but feeling genuinely satisfied when the model finally goes live and starts serving predictions correctly.

Solving Interesting Puzzles

You enjoy the intellectual challenge of figuring out why a model isn't performing, or how to best engineer a feature from messy raw data. Every new problem is a chance to flex your analytical muscles.

Being given a new dataset and a vague business problem, and enjoying the process of exploring the data, trying different models, and finding a solution.

Learning and Growing

You're always keen to pick up a new library, understand a different model architecture, or dive into a new cloud service. The idea of learning something new every week isn't daunting; it's exciting.

Taking the initiative to learn PyTorch for a new project, even though you've mostly used TensorFlow before, because you see the long-term benefit.

What frustrates people
  • The 'Last Mile Problem': You've built an amazing model, but it sits unused for weeks because the product team is swamped and can't integrate it.
  • Data Quality Quagmire: Spending 60% of your time cleaning and preparing data that's poorly documented or inconsistent, rather than building models.
  • Endless Meetings: Getting pulled into too many meetings that don't seem to have a clear outcome, eating into your actual coding time.
  • Unclear Requirements: Being asked to 'just add some AI' to a problem without a clear definition of success or what 'AI' even means in that context.
What this role does not give you
  • A perfectly structured, predictable work environment where everything is clearly defined from day one.
  • The chance to only work on greenfield, cutting-edge research projects that never need to ship.
  • A role where you're solely focused on deep theoretical research without any practical application or deployment.
  • A rigid, siloed team where you don't need to interact with other departments or explain your work.

8Who you work with

Your work directly impacts the intelligence of our products and the efficiency of our internal operations. Every model you build, every feature you engineer, helps us understand our customers better, automate tedious tasks, and make more informed strategic moves. Essentially, you're building the brains of our business, one algorithm at a time.

Inside the business
  • Senior ML Engineers (for technical guidance and code reviews)
  • Product Managers (to understand what problems we're actually trying to solve)
  • Data Engineers (because you'll rely on their data pipelines, a lot)
  • Software Engineers (who'll be integrating your models into our products)
  • QA and Testing Teams (to make sure your models don't break everything)
Outside the business
  • Cloud Platform Vendors (e.g., AWS, GCP support teams, when things go wrong)
  • Open-source Communities (you'll draw a lot from them, and maybe even contribute back)

9What you need before you start

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

  • Strong programming skills in Python, including familiarity with object-oriented programming concepts.
  • Solid understanding of data structures and algorithms, as they apply to efficient data processing.
  • Experience building and deploying at least two distinct Machine Learning models in a commercial or significant academic project.
  • Practical experience with SQL for data querying and manipulation.
  • Familiarity with version control systems, especially Git.
  • A foundational understanding of statistics and linear algebra.

10What to practise next

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

Advanced MLOps & System Reliability

Getting one model into production is one thing; reliably operating dozens of models at scale is another. The focus shifts to building robust, automated, and self-healing ML systems that can handle real-world chaos.

Automated Model Retraining & Versioning · Canary Deployments & A/B Testing for Models · Distributed Training & Inference

  • This quarter: Take ownership of improving one aspect of an existing model's deployment or monitoring setup.
  • Next quarter: Research and propose a new MLOps tool or technique that could improve our team's efficiency or model reliability.
  • Within 6 months: Lead the design and implementation of a new automated retraining pipeline for a critical model.

Quick win: Spend an hour exploring the advanced features of MLflow or Weights & Biases that you haven't used yet. There's usually more there than meets the eye.

Specialised Deep Learning Architectures

While general ML is great, many of the really impactful problems require deeper knowledge in specific areas like Natural Language Processing (NLP) or Computer Vision. As you progress, you'll likely specialise.

Transformer Models (for NLP) · Convolutional Neural Networks (CNNs for CV) · Reinforcement Learning Basics

  • This quarter: Pick one area (e.g., NLP or Computer Vision) that genuinely interests you and start a personal project.
  • Next quarter: Take an online course or read a foundational book on your chosen specialisation.
  • Within 6 months: Propose a project where you can apply your new specialised knowledge to a business problem.

Quick win: Find a relevant Kaggle competition in your chosen specialisation and try to implement a basic solution. It's a great way to learn by doing.

11Staying current once you are in

What people here do to keep up
  • Actively participate in online ML communities (e.g., Kaggle, Stack Overflow, specific Discord/Slack channels).
  • Contribute to open-source ML projects (even small bug fixes or documentation improvements count!).
  • Attend relevant webinars, conferences (even virtual ones), or local meetups to stay current and network.
  • Dedicate time to personal ML projects that challenge you and allow you to explore new techniques or tools.
  • Regularly engage with ML research papers and blogs to understand emerging trends and methodologies.

12The rising capability

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.

Prompt Engineering & LLM Integration

Honestly, this is already happening, not just 'emerging'. Competitors are using large language models (LLMs) to draft reports in 10 minutes that used to take 2 hours, or to generate code snippets that save hours of manual typing. Engineers who figure this out will simply outproduce their peers.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG (Retrieval Augmented Generation)
  • Output Validation & Hallucination Detection
  • Prompt Chaining

Responsible AI Practices (Bias & Fairness)

As our models get more powerful and impact more users, the ethical implications become a bigger deal. Regulators are paying attention, and customers care about fairness. Ignoring bias isn't an option anymore; it's a business risk and a reputational one.

  • Algorithmic Bias Detection
  • Fairness Metrics
  • Mitigation Strategies
  • Explainability (XAI) Basics
  • Model Cards & Documentation

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • MLOps & Productionisation Fundamentals
  • Statistical Modelling & Experiment Design
  • Feature Engineering & Data Preprocessing
  • Core Machine Learning Algorithms
  • Model Evaluation & Interpretation

14The pathway

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. 1

    Junior Machine Learning Engineer

    1-2 years

    Skills to master

    • Solid Python programming, basic ML algorithm implementation, data preprocessing, understanding of version control (Git), and effective debugging techniques.

    You're ready to move on when

    • Consistently delivers well-tested code for assigned tasks with minimal supervision.
    • Proactively identifies and resolves minor technical issues independently.
    • Actively seeks feedback and applies it to improve code quality and model performance.
    • Can clearly articulate the purpose and basic workings of models they've built.
  2. 2

    Data Analyst (with ML focus)

    2-3 years

    Skills to master

    • Strong SQL, data visualisation, statistical analysis, basic Python scripting for data manipulation, and an understanding of how business questions translate into data problems. Needs to build out ML modelling and deployment skills.

    You're ready to move on when

    • Has built basic predictive models (e.g., regression, classification) as part of their analytical work.
    • Demonstrates a strong desire and aptitude to move into model building and deployment.
    • Has started learning MLOps concepts and cloud ML platforms in their own time.
    • Can clearly explain the business impact of their analytical work.
  3. 3

    Software Engineer (transitioning to ML)

    1-2 years (focused on ML upskilling)

    Skills to master

    • Deep programming expertise, software engineering best practices (testing, CI/CD), system design. Needs to acquire core ML theory, algorithms, and MLOps specific to model lifecycle management.

    You're ready to move on when

    • Has contributed to ML-related projects (e.g., building data pipelines, integrating ML APIs) in a software engineering capacity.
    • Has completed relevant ML courses or certifications and applied them in personal projects.
    • Demonstrates a strong understanding of data science fundamentals and statistical concepts.
    • Can articulate how software engineering principles apply to building robust ML systems.

15Where this role leads

The long view: This work can lead to a lead or staff scientist role, a machine learning manager or principal scientist position, or a director of machine learning or AI role.

16Pay and demand

Pay & demand

Typical pay £56,914 a year Source: ONS Annual Survey of Hours and Earnings, 2025 (provisional), under the Open Government Licence v3.0

The figure is the median for full-time employees in the ONS occupation this job title codes to (Programmers and software development professionals), from the April 2025 survey, about six months old when published, as ASHE always is. It is that occupation's middle, not this role's. Half earn more.

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 Machine Learning Engineer is actually changing.

Where it leads

Roles this leads to

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Machine Learning Methods and Models in Data Science, worked against Machine Learning Engineer.

Machine Learning Methods and Models in Data Science Level 3

Applied to your work in Machine Learning Engineer

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
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 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.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

Start my Machine Learning Engineer plan, free
See Your Progress Grow Illustration
Machine Learning Engineer
  • MLOps & Productionisation Fundamentals
  • Statistical Modelling & Experiment Design
  • Feature Engineering & Data Preprocessing
  • Core Machine Learning Algorithms
  • Model Evaluation & Interpretation
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.

17The 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

Machine Learning Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior Machine Learning Engineer (L3)

    2-3 years from this role

    You'll move from owning individual projects to leading entire workstreams, mentoring juniors, and making significant technical design decisions. The scope of your impact and autonomy grows considerably.

    • Advanced MLOps & System Design Designing robust, scalable, and reliable ML production systems.
    • Architectural Decision-Making Choosing appropriate ML architectures and platforms for complex problems.
    • Performance Optimisation Deep understanding of how to optimise models and pipelines for speed and cost.
    • Cross-functional Project Leadership Leading ML initiatives that involve multiple engineering teams.

  2. MLOps Engineer

    2-4 years from this role

    This is a specialisation. You'd shift your focus from building individual models to building and maintaining the infrastructure and pipelines that enable all models to be developed, deployed, and monitored efficiently and reliably.

    • Containerisation & Orchestration (Docker, Kubernetes) Expertise in managing containerised ML workloads.
    • Advanced CI/CD/CT Pipeline Design Building and optimising automated pipelines for ML.
    • Cloud Infrastructure Expertise Deep knowledge of cloud services for compute, storage, and networking.
    • Model Monitoring & Alerting Systems Designing and implementing robust monitoring for model health and performance.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of an ML Engineer's job involves repetitive coding, digging through research papers, and trying to make sense of cryptic error messages. What if you could get a significant chunk of that time back? We're not talking about replacing you; we're talking about giving you superpowers.

We're big believers in using AI to make our own engineers more productive. For a Machine Learning Engineer, this means tapping into tools that can write boilerplate code, summarise complex research, help you debug faster, and even draft documentation. It's about letting you focus on the hard, creative problems, not the tedious bits.

Code Generation & Refactoring

Imagine having a co-pilot that can suggest entire blocks of Python code, write unit tests, or refactor messy functions with a single prompt. Tools like GitHub Copilot and Tabnine can dramatically speed up your coding, especially for common ML patterns like data loading, feature scaling, or model training loops. It's like having another pair of hands that knows all the best practices.

Research & Paper Summarisation

Keeping up with the latest ML research is a full-time job in itself. Instead of sifting through dozens of arXiv papers, use an LLM-powered agent to summarise the key findings, methodologies, and limitations of relevant research. You can quickly grasp the essence of new techniques and decide which ones are worth a deeper dive, saving hours of reading time.

Documentation & Explanation

Nobody loves writing documentation, but it's essential. Use AI assistants to generate initial drafts of model cards, API documentation, or even inline code comments. You can feed it your code and a prompt, and it'll give you a coherent explanation, freeing you up to refine and add the crucial details. It's a huge time-saver for that necessary evil.

Debugging Assistant

Stuck on a cryptic traceback? Paste your error message and relevant code into an AI assistant, and it can often suggest potential causes and solutions much faster than a manual search. It's like having a senior engineer on call 24/7 to help you diagnose and fix issues, getting your models back on track quicker.

Common questions

Common questions

How do you become a Machine Learning Engineer?

Common routes in include Junior Machine Learning Engineer (1-2 years), Data Analyst (with ML focus) (2-3 years) and Software Engineer (transitioning to ML) (1-2 years (focused on ML upskilling)). Times vary with prior experience.

Where can a Machine Learning Engineer progress to?

This role can lead on to Senior Machine Learning Engineer (2-3 years from this role) and MLOps Engineer (2-4 years from this role), depending on the skills you build.

What new skills matter most for a Machine Learning Engineer?

Increasingly, Prompt Engineering & LLM Integration and Responsible AI Practices (Bias & Fairness). These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

18Where to go from here

Other roles at Level 3

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

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built in this role apply across many industries, including fintech, healthcare, e-commerce and logistics. From here, you could move into a specialised domain, or into a broader data science or software engineering leadership role.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

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. Start my Machine Learning Engineer plan, free What happens next: a free account, then we build your plan as you set up. 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.