Machine Learning Engineer (National Security)

SFIA 3 to 5

Location: Available at Multiple Sites across South West, North West or Greater London

Hybrid with 3 days a week high-side access

£80,000 to £130,000 base (depending on experience)

Most ML roles talk a big game about being “cutting edge.” Then you dig deeper and realise it’s mostly proof-of-concept work, internal dashboards, or commercial problems dressed up as innovation.

This role is actually different.

You’ll be joining a modern, R&D-friendly environment known for looking after its people.

Engineers rotate across projects so they never stagnate. The culture is younger, supportive and progression-led.

If you’re already cleared to operate at the highest levels of UK National Security and want your AI/ML work to matter, this is where the interesting problems live.

What you’ll be doing

Distilled from the official spec :

• Designing, developing and deploying ML models for real National Security use cases
• Using LLMs to build agents, improve retrieval and enhance user interaction
• Running research sprints, reading papers and prototyping new algorithms
• Applying MLOps best practice to build scalable, secure, maintainable pipelines
• Translating stakeholder problems into workable AI solutions

This sits cleanly between ML engineering, MLOps and applied research. Not academic-only.

What you’ll bring

You don’t need everything below — but you should recognise a good chunk of it:

• Strong Python engineering skills
• Hands-on model development with PyTorch, TensorFlow or scikit-learn
• Experience working with LLMs, prompt engineering, RAG, fine-tuning or LangChain/LangGraph
• MLOps experience with MLflow, DVC or Weights & Biases
• Experience deploying ML solutions on AWS
• Containerisation experience (Docker)
• Ability to read research papers and turn them into working prototypes
• Strong grounding in evaluation, monitoring and retraining
• Clear communication and comfort engaging with non-technical stakeholders

Why people join this team

Consistent themes across engineering disciplines:

• A younger, modern culture compared with typical Defence suppliers
• Real R&D autonomy and space to experiment
• Rotation across programmes to keep skills broad
• Stable long-term work backed by a major parent company
• A healthy approach to hybrid high-side working

This is the closest you get to R&D freedom inside a structured Defence environment.

Non-negotiables

• Must meet the criteria for the highest level of UK security vetting within National Security (if you know, you know...)
• Must be a sole UK national
• Must be willing to work high-side when required (typically hybrid basis)

This isn't right for...

• Poor communicators
• People who avoid stakeholder interaction
• Pure research backgrounds with no engineering delivery

Inclusion

If you don’t tick every single requirement, you’re still encouraged to apply.

People from underrepresented groups often opt out even when they are fully capable.

If you’re already cleared for high-side work and have strong ML or MLOps foundations, you’ll be taken seriously. Adjustments can be made throughout the hiring process.

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