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Vacantes / Callosum

Research Engineer, Evals - Member of Technical Staff

Callosum·Londonmid

En corto

  • →Ingeniero de investigación enfocada en evaluar sistemas agenticos con alta complejidad.
  • →Diseña pruebas y métricas rigurosas para detectar fallos en sistemas de IA autónomos.
  • →Trabajas directamente con hardware heterogéneo y modelos de vanguardia en un entorno de innovación sin precedentes.

Fluent English required for collaboration across global teams.

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Las preguntas que te van a hacer

1. ¿Cómo evaluarías la consistencia de un sistema agente en un entorno no estacionario usando métricas cuantitativas?

2. ¿Qué métodos usarías para aislar fallos en un flujo de trabajo de múltiples pasos en un agente de IA?

3. ¿Cómo diseñarías un benchmark que capture el uso de herramientas en tareas de larga duración?

🔒 +7 preguntas más

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¿Qué piden?

  • ✓Experiencia en evaluación de sistemas de IA, especialmente agenticos.
  • ✓Dominio de métricas de evaluación y análisis de rendimiento.
  • ✓Capacidad para diseñar experimentos controlados con resultados reproducibles.
  • ✓Conocimiento en modelos de lenguaje y sistemas multi-etapa.
  • ✓Habilidades en Python y análisis de datos.
  • ✓Inclinación por la investigación científica y el pensamiento crítico.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonPyTorchTensorFlowHugging FaceDockerKubernetesCerebrasd-MatrixIntelNVIDIA

¿A quién escribirle en Callosum?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 orders of magnitude improvements in AI systems will come through application-aware orchestration across heterogeneous models and hardware. Our team is developing the science that makes that possible: a principled, evidence-based method for building agentic systems automatically at unprecedented scale. The team tackles these problems on two fronts. We build the tools to analyse and evaluate agentic systems rigorously enough to say where and why they go wrong, and we use what we learn to design better ones. We are not simply building a better harness; the best harness will change with every new model, task and generation of silicon. We are building the layer beneath it, so that design decisions follow from evidence rather than taste. This role owns the first of those fronts. Agentic evaluation today is not up to the job: benchmarks saturate, scores move for reasons unrelated to capability, results fail to reproduce across runs, and a single number tells you nothing about which step went wrong. Every design decision the rest of the team makes rests on the quality of that instrument. You will build measures whose construct we can defend, whose variance we understand, and which localise a failure rather than merely scoring it. The mandate is broad, and most of the questions inside it are still unanswered. You will have wide latitude to choose problems, and your results will shape what the company builds in the future.

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