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full-timeML Research Engineer - Member Of Technical Staff
Callosum · London
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. 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 hypothe
| Role | ML Research Engineer - Member Of Technical Staff |
|---|---|
| Company | Callosum |
| Location | London |
| Type | full-time |
| Compensation | Not disclosed |
| Deadline | Rolling |
Typical process for this type of role
A general guide — the exact steps for this specific listing may vary; check the original posting for details.
- 1ApplicationSubmit your resume through the apply link.
- 2ScreeningRecruiter reviews your background against the role.
- 3AssessmentA technical test, assignment, or coding round, depending on the role.
- 4Interview(s)One or more rounds with the hiring team.
- 5OfferOffer letter with compensation and start date.
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