Machine Learning Engineer (National Security)

Forward Role; Cheltenham; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Forward Role is seeking a Machine Learning Engineer (National Security) to join specialist teams delivering ML from research to production in secure UK environments.

You'll work across Python, PyTorch/TensorFlow, MLOps, AWS, Docker, and CI/CD, translating research into real-world customer solutions while collaborating with multidisciplinary teams and maintaining SC/DV/eDV clearance as appropriate.

Qualifications

• Experience with Python and ML frameworks (PyTorch, TensorFlow or scikit-learn).
• Familiarity with ML research, rapid prototyping and deployment
• Experience with MLOps, production ML pipelines and monitoring

Responsibilities

• Develop and iterate ML models from research to production in national security contexts.
• Experiment with LLMs, RAG, agents, fine-tuning and evaluation.
• Build and maintain production ML pipelines with MLflow, DVC or Weights & Biases.

Skills

Python

PyTorch

TensorFlow

scikit-learn

LLMs

RAG

fine-tuning

MLOps

MLflow

DVC

Weights & Biases

AWS

Docker

Linux

CI/CD

Tools

PyTorch

TensorFlow

scikit-learn

MLflow

DVC

Weights & Biases

AWS

Docker

Linux

CI/CD

Job description

Machine Learning Engineer (National Security)

Location: Opportunities across key UK National Security hubs

Level: Mid-level through to Senior / Lead
Clearance: Active SC minimum | DV/eDV particularly desirable
Working pattern: Hybrid / secure-site working depending on programme

There's a big difference between experimenting with Machine Learning and seeing it survive contact with the real world.

I'm supporting specialist AI/ML teams working across that entire journey — research, rapid prototyping, productionisation and deployment into genuinely challenging National Security environments.

This isn't about joining a generic consultancy team that happens to have an AI project.

I'm working with some excellent specialist engineering and research teams where AI/ML is core to what they do — surrounded by people who read the papers, experiment with emerging technology and genuinely care about keeping their technical edge.

What you'll be working across

• Python and modern ML frameworks including PyTorch, TensorFlow or scikit-learn
• LLMs, RAG, agents, fine-tuning and evaluation
• ML research, algorithm development and rapid prototyping
• MLOps and production ML pipelines
• Model deployment, monitoring and lifecycle tooling such as MLflow, DVC or Weights & Biases
• AWS, Docker, Linux and CI/CD
• Translating emerging research into solutions that work against real customer problems

These environments also place real value on engineers who can engage with stakeholders and work across multidisciplinary teams rather than disappearing into a purely research-focused silo.

Who tends to thrive?

You don't need to have used every framework above.

I'm much more interested in smart, curious engineers who enjoy learning, can move between research and engineering, and actively keep up with a field that's changing incredibly quickly.

If you've reached the point where you'd like to see more of your work make it beyond the notebook or PoC stage — and ultimately understand the impact it has in the real world — this could be a very interesting next step.

Clearance

You'll need active SC clearance as a minimum for the opportunities I'm currently supporting, with active DV/eDV particularly desirable for work on higher-side programmes.

If you're already working in ML/AI within the secure community and curious about what some of the specialist labs and engineering teams are doing differently, feel free to get in touch.

Even if you're not actively looking, I'm happy to share an honest view of the market and where your background might fit.