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# DV Cleared Machine Learning Engineer - Spiking Neural Networks

- **Company:** MFK Recruitment
- **Location:** Cambridgeshire, UK
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, Artificial Neural Networks, Computer Vision, C++ (Programming Language), Nvidia CUDA, Python (Programming Language), Machine Learning, Software Architecture, Sensor Fusion, Software Engineering, Pytorch, Safety Critical Systems, Deep Learning
- **Published:** November 6, 2026
- **Apply:** https://www.securityclearedjobs.com/job/802086824/dv-cleared-machine-learning-engineer-spiking-neural-networks/

## About the Role

* Active UK DV clearance.
 * Strong practical knowledge of Spiking Neural Networks or neuromorphic computing.
 * Experience implementing or modifying SNN architectures.
 * Understanding of neuron models such as LIF, Izhikevich or related approaches.
 * Knowledge of learning methods such as STDP, Hebbian learning or surrogate-gradient training.
 * Strong Python development skills.
 * Experience with PyTorch or an SNN framework such as snnTorch, Norse, Nengo, Brian2 or Lava.
 * Strong mathematical and analytical ability.
 * Evidence of building working software rather than concentrating entirely on theoretical research.
 * Desirable experience
 * Computer vision or machine-vision applications.
 * Event-based cameras or event-driven sensing.
 * CNNs, Vision Transformers or conventional deep learning.
 * C or C++ development.
 * CUDA or GPU optimisation.
 * Real-time or embedded deployment.
 * Performance-critical simulation engines.
 * Robotics, autonomous systems, UAVs or sensor fusion.
 * Defence, aerospace or safety-critical systems.

Research backgrounds will be considered where there is strong evidence of practical implementation and high-quality software engineering.

Candidates must already hold active UK DV clearance and be comfortable working fully onsite at the customer's Cambridgeshire office.

## Description

* Design and implement Spiking Neural Network models and supporting software.
 * Develop reusable components for training, simulation and inference.
 * Implement neuron models, synaptic behaviour and learning mechanisms.
 * Apply SNN techniques to computer vision, perception and event-driven data.
 * Benchmark accuracy, latency, memory usage and computational performance.
 * Optimise implementations for CPUs, GPUs and specialist hardware.
 * Develop robust testing, validation and profiling frameworks.
 * Translate research concepts into maintainable engineering solutions.
 * Work with computer vision, software and systems engineers.
 * Produce technical documentation and contribute to architectural decisions.