Evolutionary Optimisation - Member of Technical Staff

Callosum·Londonmid

In short

→Construyes un sistema autónomo que mejora software de IA mediante búsqueda guiada por LLM.
→Trabajas en tiempo real con chips heterogéneos como Cerebras, NVIDIA, AMD y Tenstorrent.
→Tu sistema optimiza desde kernels hasta políticas de escalado con IA evolutiva.

Proficiency in English required for technical collaboration.

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The questions they'll ask you

1. ¿Cómo diseñarías un bucle de evolución programática para optimizar una política de caché KV en un entorno de inferencia heterogéneo?

🔒 +7 more questions

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💵 USD · Remote · No visa

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What they ask for

✓Experiencia sólida en optimización combinatoria y algoritmos de búsqueda.
✓Conocimiento profundo de LLMs y cómo integrarlos en bucles de optimización.
✓Habilidades avanzadas en Python y sistemas de bajo nivel (C/C++)
✓Capacidad para diseñar benchmarks continuos y medir mejoras de manera empírica.
✓Experiencia con aceleradores heterogéneos o sistemas de cómputo especializados.
✓Proyección hacia la investigación y desarrollo de nuevas arquitecturas de optimización.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonC/C++LLMsGenetic AlgorithmsReinforcement LearningHardware AcceleratorsHeterogeneous ComputeKernel OptimizationInference RuntimeKV Caching

Who should you write to at Callosum?

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About Us 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. Callosum 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. About the Role Callosum 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 Callosum’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.

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