Founding Machine Learning Researcher (Optimisation & DL)

Founding Machine Learning Research Scientist / Engineer - Optimisation & Bayesian Deep Learning

London - Hybrid (Flexible, primarily Remote)

I'm hiring for an AI start-up focused on continual learning. They're looking for a Research Scientist/ Engineer to work on the mathematical and algorithmic foundations of this programme, combining advanced optimisation, probabilistic modelling and large-scale deep-learning experimentation.

You will join a small, highly technical group and take ownership of research from theoretical development through to implementation and empirical validation.

What you’ll work on

• Develop new second-order and curvature-aware optimisation methods.
• Research scalable approximate Bayesian inference for neural networks.
• Work with Fisher, Hessian and related curvature approximations.
• Develop uncertainty-estimation and posterior-approximation techniques.
• Investigate optimiser design, training dynamics and model adaptation.
• Implement new learning algorithms in modern deep-learning frameworks.
• Evaluate new methods across increasingly large neural networks.
• Help shape the company’s wider research direction.

What we’re looking for

• Deep expertise in optimisation, Bayesian deep learning or a closely related area.
• Experience with natural gradients, KFAC, Shampoo, SOAP or similar techniques.
• Knowledge of approximate inference and uncertainty estimation.
• Strong mathematical and numerical understanding of deep learning.
• Excellent Python and modern ML framework experience.
• Ability to translate research ideas into reliable experimental code.
• A PhD or equivalent research experience in a highly technical discipline.

Useful additional experience

• Training or adapting large language models.
• Continual learning, active learning or reinforcement learning.
• Distributed multi-GPU or multi-node training.
• Optimising memory, communication or optimiser state.
• Scaling novel algorithms from small experiments to large models.

Depth in optimisation and probabilistic machine learning is more important than experience with any particular model family.

• Work on fundamental, unsolved problems in machine learning.
• Own research from mathematical development through to large-scale experimentation.
• Join a small, high-calibre team with significant technical autonomy.
• Help shape the research culture of an ambitious early-stage AI company.
• Receive a highly competitive salary and meaningful equity.

#J-18808-Ljbffr