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Callosum

Orchestrates AI workloads across specialized silicon

Evolutionary Optimisation - Member of Technical Staff

AI ResearchConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • Strong foundations in optimisation and search, including familiarity with evolutionary, stochastic, combinatorial, or other non-gradient-based methods.
  • Ability to reason about search itself, including 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 large language models as the solution to every problem.

Responsibilities

  • 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.

About the Company

Callosum

Orchestrates AI workloads across specialized silicon

Callosum builds AI infrastructure that unifies different AI models and hardware chips to run workloads on the best combination of model and chip for each task. Its Tailored Inference APIs route tasks to the optimal model–chip pair while honoring cost, energy, and latency constraints, using partners like Cerebras and Rebellions for specialized silicon. Unlike approaches that rely on one model and uniform hardware, Callosum orchestrates multiple models and chips to maximize efficiency. The company's goal is to enable scalable, cost-efficient AI inference across heterogeneous hardware by intelligently matching tasks to the right combination of model and accelerator.

Company size
11-50
Company stage
Seed
Total funding
$110.3M
Headquarters
London, United Kingdom
Founded
2025

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