16 days ago
Responsibilities
- Develop the core program evolution loop for generating, evaluating, selecting, and evolving candidates over long optimisation runs.
- Design search strategies that balance exploration, exploitation, diversity, and sample efficiency across irregular optimisation spaces.
- Build continuous benchmarks to measure optimiser performance across internal workloads and standalone problems.
- Improve the optimiser through empirical measurement and make regressions visible.
- Turn the optimisation system into reliable, reusable internal infrastructure for new verifiable domains.
Requirements
- Strong foundations in optimisation and search, including evolutionary, stochastic, combinatorial, or other non-gradient-based methods.
- Ability to reason about exploration versus exploitation, diversity, pruning, objective design, and the suitability of different optimisation strategies.
- Strong engineering instincts and experience turning experimental systems into reusable, measurable, and reliable software.
- Demonstrated ability to draw on techniques across optimisation, algorithms, and machine learning rather than relying on LLMs for every problem.
Benefits
- Competitive salary determined by skills and experience.
- Equity and ownership.
- Private healthcare.
- Visa sponsorship and relocation benefits.
- In-person work at the company’s London office with provided tools, workspace, and setup.
Categories
AI Research
