Research Engineer, Evals - 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.

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.

What You'll Build

• Design benchmarks and evaluation suites for agentic behaviour – multi-turn, long-horizon, tool-using, operating in non-stationary environments – including the properties that are hard to score, such as reliability and recovery from error

• Build the methodology as well as the harness: statistical power, variance across runs, contamination and construct validity

• Be the independent voice on the quality of our own results. Red-team our evaluations, design the sanity checks that catch a flattering number before it reaches a decision.

• Turn raw traces into structured evidence. Failure taxonomies, behavioural signatures, and attribution of an outcome to the decision that caused it

• Move from measurement to prediction: infer what a system can do from partial evidence, and estimate performance on a task before running it. This rests on defining what each model is genuinely good at, what benchmarks really measure, and what capability profile a real compound task demands

• Build evaluation and observability infrastructure as durable instruments the whole company works from

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

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

• Statistical discipline. Hypotheses stated in advance, uncertainty quantified, and running comparisons that isolate one variable

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

• Strong communication skills. You are 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.