Founding ML Researcher - Optimisation & Bayesian DL Remote

SR2; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Equity

Remote-friendly

Hybrid work

Job summary

SR2 is seeking a Founding Machine Learning Research Scientist / Engineer to join our London-based, hybrid/remote-enabled team. You will tackle optimisation, probabilistic modelling and large-scale deep-learning experimentation, taking ideas from theory to production-ready code.

You'll own research from mathematical development through to empirical validation, work on second-order methods, Bayesian inference and uncertainty estimation, and help shape the company's long-term research strategy.

Qualifications

• PhD or equivalent research experience in a highly technical discipline.
• Deep expertise in optimisation, Bayesian deep learning or a closely related area.
• Excellent Python and modern ML framework experience.
• Ability to translate research ideas into reliable experimental code.

Responsibilities

• 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.

Skills

Optimization

Bayesian DL

Python

Deep learning

Education

PhD or equivalent

Tools

ML frameworks

Job description

SR2 is seeking a Founding Machine Learning Research Scientist / Engineer to join our London-based, hybrid/remote-enabled team. You will tackle optimisation, probabilistic modelling and large-scale deep-learning experimentation, taking ideas from theory to production-ready code.

You'll own research from mathematical development through to empirical validation, work on second-order methods, Bayesian inference and uncertainty estimation, and help shape the company's long-term research strategy.