AI Research Engineer

Our client, an early-stage AI research lab, is hiring Research Scientists and Research Engineers to work on brain-inspired, mathematically grounded approaches to making AI more efficient, with direct impact on how future models store and compute.
Focus areas (one or more):
Brain-inspired architectures (state-space, recurrent, associative-memory and long-context models)
Neural memory and dynamical systems modelling
Optimisation and neural-network training dynamics
Efficient AI (quantisation, sparsity, model compression, low-precision computation)
Spiking systems, neuromorphic computing and computational neuroscience
Hardware-aware machine learning (memory bandwidth, inference efficiency, energy modelling)
Experimental design and evaluation (baselines, ablations, robustness, computational cost)
Requirements:
Bachelor’s, Master’s or PhD in Maths, Neuroscience, Computer Science, Physics, Engineering or a closely related quantitative field
Strong foundations in several of: linear algebra, probability, optimisation, dynamical systems, numerical methods, information theory or statistical learning
Strong Python and PyTorch (or JAX) experience, with a solid understanding of transformers and LLM architectures
Rigorous experimental mindset, with the ability to move between mathematical reasoning, experimentation and implementation
Research publications, open-source contributions or substantial independent projects (industry experience not essential)
Location: London. Full-time, permanent.
If this role sounds interesting to you, please reach out to Charles Duran.