Evolutionary Optimisation - Member of Technical Staff

1 нед. назад

101k–192k GBP / yearUnited KingdomEuropeLeadOnsite

machine learningevolutionary algorithmspythonsoftware orchestration

Research role focused on evolutionary optimisation within AI systems combining hardware and software innovation.

О компании

We’re living through a Cambrian explosion of intelligence: new models and new chips, each specialised for different tasks, are arriving all at once. The result is a new era for AI, one of radical heterogeneity.
is the Intelligent Systems Company. We believe the next generation of AI won't be defined by any single model or chip, but by intelligent systems in which hardware and intelligence co-evolve. We are building the infrastructure that unifies heterogeneous compute across the full stack. This opens a new axis of scaling intelligence: a dynamic system that tailors itself to what each workload actually needs, whether that's speed, cost, precision, or whatever unit comes next.
The last era scaled on a different bet: one bigger model, more of the same chip, more data. That bet is running into structural limits. Frontier models offer extraordinary capability at unsustainable cost, one that today's monolithic infrastructure was never designed to serve.
Our founding principle is that intelligence comes from many specialised systems working together, not from any single component. We build the software orchestration layer that co-evolves models, workflows and silicon into one system, delivering inference tailored to every workload, and demonstrating orders-of-magnitude leaps in capability and cost.
Because our software spans the full stack, our engineering team works directly with heterogeneous accelerators and frontier silicon, including Cerebras, d-Matrix, Intel, NVIDIA, AMD, Normal Computing, Tenstorrent, GreatSky, and Mixx. We are not stopping at today's chips: each new generation of silicon unlocks algorithms that couldn't run before, and we intend to be first to them, every time. If we get it right, it will belong to everyone building on it - not to any single vendor.
In our latest funding round, we raised $100M, led by Atomico with participation from Plural, DCVC and the UK Sovereign AI Fund’s first investment. With this, we are building the infrastructure for the next era of intelligence.
We are engineers and scientists based in London, working across the full depth of the stack. We are curious, intellectually honest, and building what doesn't exist yet. If you thrive on uncharted territory and are energised by the scale of the challenge, we'd love to hear from you.

Обязанности

believes that the next generation of AI systems will be co-optimised across models, software, and hardware. As we operate across increasingly heterogeneous workloads and accelerators, the number of possible system configurations grows combinatorially. Manually exploring that space does not scale – we need systems that can search it themselves.
This role owns ’s program evolution and optimisation loop: an LLM-guided search system that iteratively generates, evaluates, and improves programs against verifiable objectives. We already use this approach across kernels, inference runtimes, scheduling and scaling policies, KV caching strategies, and more. You will develop the loop both as a reusable internal optimisation system and as a research object in its own right, improving how it searches, measuring what makes it effective, and pushing its capabilities on increasingly difficult optimisation and reasoning problems.

Условия

Competitive Salary, determined by skills and experience
Equity & Ownership
Private healthcare
We offer Visa sponsorship and relocation benefits to hire the best in the world
We work in person at our London office. You'll have the tools, space and setup to do your best work, and if you have specific needs, just tell us
We're committed to building an inclusive workplace where everyone feels welcome, and believe in equal opportunities for all.

Другое

Develop the core program evolution loop, improving how candidates are generated, evaluated, selected, and evolved over long optimisation runs
Design search strategies that balance exploration, exploitation, diversity, and sample efficiency across vast and irregular optimisation spaces
Build continuous benchmarks that measure the optimiser across internal workloads and standalone problems, making improvements and regressions empirically visible
Turn the system into reliable internal infrastructure that can optimise new verifiable domains as a reusable internal tool that other team members can scale to new problems
Strong foundations in optimisation and search, including familiarity with evolutionary, stochastic, combinatorial, or other non-gradient-based methods
Ability to reason about search itself i.e. exploration versus exploitation, diversity, pruning, objective design, and when different optimisation strategies are appropriate
Strong engineering instincts, with experience turning experimental systems into reusable, measurable, and reliable software
A demonstrable tendency to draw from techniques across optimisation, algorithms, and machine learning rather than treating LLMs as the solution to every problem