ML Research Engineer - Member of Technical Staff

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.

Today, designing these systems is a craft. Decisions about memory management, task decomposition, tool use and inter-agent coordination are made on intuition and convention. The results work until they don't, and it is rarely clear in advance which failure comes next.

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.

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.

What You'll Build

• Design and run experiments that isolate why agentic systems fail - across behaviour, traces and activations - and turn those findings into interventions that measurably move intelligence, cost and reliability

• Attack one or more of our core research themes: context and memory management; agent steering, task decomposition and specialisation; continual learning during deployment; and inter-agent communication

• Build agent analysis and evaluation infrastructure - observability, behaviour analysis, evaluation harnesses, auditing - as durable instruments the whole company works from, not one-off scripts

• Publish. Evaluation results, methods and discoveries, as both company assets and public evidence

• Work across team boundaries in both directions: take a vertical domain-specific application problem and design systems which run at unpreceding scale at lowest cost and latency; or invent novel model and agent architecture which exploit emerging unconventional silicons beyond GPU

What You'll Bring

• Evidence that you can run research of your own: you take an open question, design the experiments that settle it, and produce results other people can build on. Where you learned to do that matters less to us than that you can

• Deep hands-on experience with LLMs in agentic settings: multi-step tasks, tool use, long horizons, and the specific ways all of it breaks

• Real experimental discipline. Stated hypotheses, controlled comparisons, ablations that isolate one variable, honest uncertainty, and the instinct to distinguish a genuine effect from prompt luck or benchmark noise

• Strong engineering. You write the code that runs your own experiments and you are comfortable working inside a substantial shared codebase

• Strong communication skills. Able to turn research findings into clear, prioritised guidance for the teams who will act on them, and to write them up for a wider audience

What We Offer

• 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.