ML Research Engineer - Member of Technical Staff

Über diese Stelle

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.…

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