L6Apprenticeship79518 approved providers

The Level 6 Machine learning engineer, and the 18 providers delivering it.

The ML Engineer gathers data from different sources to design, build, deploy and validate machine learning and or artificial intelligence solutions.

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About this apprenticeship

What this apprenticeship covers

Apprentices learn to design, build, deploy and monitor machine learning and AI solutions across the full model lifecycle. That includes sourcing and preparing data responsibly, selecting appropriate algorithms and features, training and evaluating models, and managing deployment into live environments. The apprenticeship also covers security and vulnerability assessment, data governance, ethics, and compliance requirements. Apprentices gain hands-on experience with ML pipelines, experiment tracking, model versioning, and performance monitoring, alongside project management frameworks such as CRISP-ML.

Day-to-day responsibilities

Week to week, an apprentice in this role works across the ML pipeline: cleaning and preparing datasets, experimenting with model architectures, evaluating performance metrics, and supporting deployment into production. They document model decisions and risks, contribute to technical reviews, and communicate findings to both technical colleagues and non-technical stakeholders. Regular collaboration with data scientists, data engineers, software engineers, and product managers is typical, as is monitoring live models for drift or degradation and proposing adjustments when performance drops.

Career outlook

Completing this apprenticeship leads naturally into roles such as machine learning engineer, ML operations engineer, AI engineer, or big data engineer. From there, progression often moves toward senior engineering positions, ML platform or infrastructure roles, or specialist AI research and development positions. Employers span virtually every sector, including financial services, healthcare, retail, agriculture, and the public sector, anywhere that data-driven automation creates operational value. Larger technology consultancies, NHS trusts, financial institutions, and government departments are among the most active hirers at this level.

Career outcomes

Roles after completion

Completers typically move into roles such as Machine Learning Engineer, MLOps Engineer, AI Engineer, or Big Data Engineer. Day-to-day responsibilities include building and deploying ML models, maintaining data pipelines, monitoring model performance in production, and working alongside data scientists and software engineers to integrate ML systems into live products and services.

Progression paths

Within three to five years, most engineers progress to Senior Machine Learning Engineer or Senior MLOps Engineer, taking ownership of end-to-end model lifecycles and mentoring junior colleagues. From there, two tracks tend to open up. The leadership track leads toward AI Engineering Manager or Head of AI, with accountability for team direction and product strategy. The specialist track leads toward roles such as Principal ML Engineer or AI Architect, focusing on model design, infrastructure, and technical standards across an organisation.

Where these roles sit

Demand comes from across both public and private sectors. Financial services firms, NHS and health technology organisations, retail and e-commerce businesses, logistics providers, and defence and security agencies all hire for these roles. Employer scale ranges from early-stage AI startups to large enterprise technology teams and government digital services. Consultancies that build ML solutions for client organisations are also a significant source of vacancies.

How it's assessed

How the apprenticeship is assessed

Throughout the apprenticeship, the learner works in a real ML engineering role while building the knowledge, skills and behaviours set out in the standard. These span the full model lifecycle, from data sourcing and feature engineering through to deployment, monitoring and governance. Before final assessment can begin, both the employer and training provider must confirm the apprentice is ready, a stage commonly called the gateway. Final assessment then determines whether the apprentice can demonstrate genuine occupational competence, not just theoretical understanding. Assessment models for many standards are currently being updated as part of ongoing reforms, so check the standard's gov.uk page for the current specification.

What learners need to prepare

Building strong evidence from day-to-day work is essential throughout the programme rather than scrambling to gather it at the end. That means keeping records of real projects: model builds, deployment decisions, risk assessments, and stakeholder communications. Apprentices should work closely with their employer and training provider to understand what good evidence looks like for each part of the standard, and flag any gaps early. Regular progress reviews with both parties help ensure readiness is genuinely there before the gateway is reached.

Choosing a provider

What good looks like

Look for providers whose trainers have recent, hands-on ML engineering experience, not just data science or software development backgrounds. On the FATP profile, an achievement rate above 65% is a reasonable baseline for a technically demanding Level 6 standard; above 75% is genuinely strong. Employer satisfaction scores above 80% suggest the provider is keeping pace with what organisations actually need from ML engineers in production. Check that the curriculum covers MLOps tools, model monitoring, and deployment pipelines, not just model training. Providers running industry projects or live client casework as part of off-the-job training are worth prioritising.

Red flags to watch for

Be cautious of providers who focus heavily on data science theory but give thin coverage to deployment, monitoring, and the full ML lifecycle. If a provider cannot explain which MLOps frameworks and platforms apprentices work with, that is a concern: tools and practices in this field move fast, and a curriculum built around 2021 tooling will not serve apprentices or employers well. High apprentice volumes paired with a falling achievement rate warrants a direct question. Vague answers about how apprentices access real engineering work, rather than sandboxed tutorials, are worth pressing on.

Questions to ask before you commit

Which MLOps tools and platforms do apprentices actually work with during the programme, and when were those choices last reviewed?
How does off-the-job training cover model deployment and monitoring in a live environment, rather than just model building?
What is your current achievement rate for this standard, and how has it changed over the past two cohorts?
Can you show us examples of the kinds of ML engineering problems apprentices tackle, and whether those reflect production-grade work?
How do you handle apprentices whose employers do not yet have mature ML infrastructure to work within?
What support is in place for the final end-point assessment, and what does your EPA pass rate look like?
Do your trainers have current industry experience in ML engineering, and how do you keep the curriculum current as the field changes?

Common questions

What are the entry requirements for this apprenticeship?

There are no nationally set entry requirements for this standard, so employers set their own. In practice, most organisations expect applicants to have existing technical knowledge, typically A-levels or equivalent in a numerate subject, or prior experience in a data or software role. Some employers accept candidates who already hold a relevant Level 4 or 5 qualification. Apprentices must be employed throughout and the role must give them genuine opportunity to practise machine learning engineering duties.

How long does the apprenticeship take and how does learning fit around work?

The typical duration is 24 months, though this can vary depending on the apprentice's prior learning and how the employer structures the programme. Apprentices remain employed and earn a wage throughout. A portion of their time is dedicated to off-the-job learning, which covers theory, skills development and guided study. The exact proportion is set in the apprenticeship agreement. For the current specification, check the Skills England standard page on gov.uk.

How is the apprenticeship assessed?

Before the end-point assessment, apprentices must pass through a gateway, at which point the employer and training provider confirm the apprentice has met all knowledge, skills and behaviour requirements and is ready to be assessed. Assessment models for many Level 6 standards are subject to review under ongoing Skills England reforms, so the specific end-point assessment method may change. Check gov.uk for the current version of the assessment plan before enrolling.

How does an employer pay for this apprenticeship?

The funding band for this standard is £22,000, which is the maximum that can be drawn from the apprenticeship levy or government co-investment to cover training and assessment costs. Levy-paying employers use funds held in their Digital Apprenticeship Service account. Non-levy employers co-invest, paying 5% of the training cost while the government covers the remaining 95%. Employers with fewer than 50 staff taking on an apprentice aged 16 to 18 pay nothing; the government funds the full cost.

What does a machine learning engineer apprentice actually do day to day?

Day-to-day work involves gathering and preparing data, building and training ML models, selecting appropriate algorithms, and evaluating model performance before deployment. Once a model is live, the apprentice monitors it to check accuracy and stability, then refines or re-engineers it as needed. They also contribute to documentation, apply security and data governance practices, and communicate technical findings to colleagues including data scientists, software engineers, product managers and non-technical stakeholders.

What can an apprentice progress to after completing this apprenticeship?

Completion typically leads to roles such as machine learning engineer, ML operations engineer, AI engineer or big data engineer. From there, progression routes include senior or lead ML engineering positions, AI architecture roles, or specialist paths in areas such as natural language processing or computer vision. Some completers go on to study a Level 7 qualification or a master's degree in a related field, depending on their employer's development offer and their own career goals.

18 approved providers

Providers with a published achievement rate for this standard first, highest rate at the top. Providers without a published rate for this standard follow.

Digital Native

Employer: 3.0

Digital Native is a specialist tech apprenticeship provider delivering digital apprenticeships natio...