Machine Learning Engineer

AI / Deep Learning Engineers – Defence Hardware Integration

Location: London (with on-site requirements)

Clearance: Active SC or SC eligibility required

We are supporting a cutting-edge AI research and development organisation working at the intersection of machine learning and defence technology. The team builds and deploys advanced AI models directly into operational hardware systems across land, sea, and air platforms.

You will play a key role in bringing state-of-the-art AI models to life within real-world defence environments, supporting mission-critical systems in areas such as:

• Multi-sensor fusion (acoustic, optical, and underwater sensing)
• Computer Vision and Machine Vision systems
• Real-time model deployment onto embedded and edge hardware

Key Responsibilities:

• Build, train, and optimise deep learning models for deployment on resource-constrained hardware
• Integrate AI systems into embedded and defence-focused platforms
• Work cross-functionally with hardware and software teams to ensure robust system deployment
• Contribute to the development of next-generation defence AI systems that operate reliably and securely in the field

Required Skills & Experience:

• Proven expertise in deep learning / neural network development (e.g. PyTorch, TensorFlow)
• Experience deploying models onto embedded devices or edge hardware
• Understanding of model optimisation techniques (e.g. quantisation, pruning, hardware acceleration)
• Strong Python and/or C++ programming skills
• Must be eligible for SC clearance (existing SC/DV clearance a plus)

Desirable:

• Experience working in a defence or highly regulated environment
• Knowledge of multi-modal sensor data processing (e.g. sonar, radar, thermal imaging)
• Familiarity with safety and security standards in embedded systems

This role offers a unique opportunity to work on impactful, novel AI technologies that directly support national security and defence operations. If you’re driven by solving complex engineering challenges and pushing the boundaries of applied machine learning, we'd love to hear from you.